arkouda ¶

Notes

Objects registered with the server are immune to deletion until they are unregistered.

registered_name = None¶

reverse = False¶

special_objType = 'BitVector'¶

to_list()[source]¶: Export data to a list of string-formatted bit vectors.

to_ndarray()[source]¶: Export data to a numpy array of string-formatted bit vectors.

values¶

width = 64¶

arkouda.BitVectorizer(width=64, reverse=False)[source]¶

Make a callback (i.e. function) that can be called on an array to create a BitVector.

Parameters:

width (int) – The number of bit fields in the vector
reverse (bool) – If True, display bits from least significant (left) to most significant (right). By default, the most significant bit is the left-most bit.

Returns:

bitvectorizer – A function that takes an array and returns a BitVector instance

Return type:

callable

class arkouda.CachedAccessor(name: str, accessor)[source]¶

Custom property-like object. A descriptor for caching accessors. :param name: Namespace that will be accessed under, e.g. df.foo. :type name: str :param accessor: Class with the extension methods. :type accessor: cls

Notes

For accessor, The class’s __init__ method assumes that one of Series, DataFrame or Index as the single argument data.

class arkouda.Categorical[source]¶

Represents an array of values belonging to named categories. Converting a Strings object to Categorical often saves memory and speeds up operations, especially if there are many repeated values, at the cost of some one-time work in initialization.

Parameters:

values (Strings, Categorical, pd.Categorical) – Values to convert to categories
NAvalue (str scalar) – The value to use to represent missing/null data

categories¶

The set of category labels (determined automatically)

Type:: Strings

codes¶

The category indices of the values or -1 for N/A

Type:: pdarray, int64

permutation¶

The permutation that groups the values in the same order as categories

Type:: pdarray, int64

segments¶

When values are grouped, the starting offset of each group

Type:: pdarray, int64

size¶

The number of items in the array

Type:: Union[int,np.int64]

nlevels¶

The number of distinct categories

Type:: Union[int,np.int64]

ndim¶

The rank of the array (currently only rank 1 arrays supported)

Type:: Union[int,np.int64]

shape¶

The sizes of each dimension of the array

Type:: tuple

BinOps(*args, **kwargs)¶

frozenset() -> empty frozenset object frozenset(iterable) -> frozenset object

Build an immutable unordered collection of unique elements.

RegisterablePieces(*args, **kwargs)¶

frozenset() -> empty frozenset object frozenset(iterable) -> frozenset object

Build an immutable unordered collection of unique elements.

RequiredPieces(*args, **kwargs)¶

frozenset() -> empty frozenset object frozenset(iterable) -> frozenset object

Build an immutable unordered collection of unique elements.

argsort()[source]¶

attach(user_defined_name: str) → Categorical[source]¶

DEPRECATED Function to return a Categorical object attached to the registered name in the arkouda server which was registered using register()

Parameters:: user_defined_name (str) – user defined name which Categorical object was registered under
Returns:: The Categorical object created by re-attaching to the corresponding server components
Return type:: Categorical
Raises:: TypeError – if user_defined_name is not a string

concatenate(others: Sequence[Categorical], ordered: bool = True) → Categorical[source]¶

Merge this Categorical with other Categorical objects in the array, concatenating the arrays and synchronizing the categories.

Parameters:

others (Sequence[Categorical]) – The Categorical arrays to concatenate and merge with this one
ordered (bool) – If True (default), the arrays will be appended in the order given. If False, array data may be interleaved in blocks, which can greatly improve performance but results in non-deterministic ordering of elements.

Returns:

The merged Categorical object

Return type:

Raises:

TypeError – Raised if any others array objects are not Categorical objects

Notes

This operation can be expensive – slower than concatenating Strings.

contains(substr: bytes | str_scalars, regex: bool = False) → pdarray[source]¶

Check whether each element contains the given substring.

Parameters:

substr (Union[bytes, str_scalars]) – The substring to search for
regex (bool) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

True for elements that contain substr, False otherwise

Return type:

pdarray, bool

Raises:

TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

Notes

This method can be significantly faster than the corresponding method on Strings objects, because it searches the unique category labels instead of the full array.

dtype(*args, **kwargs)¶

DType class corresponding to the scalar type and dtype of the same name.

Please see numpy.dtype for the typical way to create dtype instances and arrays.dtypes for additional information.

endswith(substr: bytes | str_scalars, regex: bool = False) → pdarray[source]¶

Check whether each element ends with the given substring.

Parameters:

substr (Union[bytes, str_scalars]) – The substring to search for
regex (bool) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

True for elements that end with substr, False otherwise

Return type:

pdarray, bool

Raises:

TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

Notes

This method can be significantly faster than the corresponding method on Strings objects, because it searches the unique category labels instead of the full array.

equals(other) → bool_scalars[source]¶

Whether Categoricals are the same size and all entries are equal.

Parameters:: other (object) – object to compare.
Returns:: True if the Categoricals are the same, o.w. False.
Return type:: bool

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> c = Categorical(ak.array(["a", "b", "c"]))
>>> c_cpy = Categorical(ak.array(["a", "b", "c"]))
>>> c.equals(c_cpy)
True
>>> c2 = Categorical(ak.array(["a", "x", "c"]))
>>> c.equals(c2)
False

from_codes(codes: pdarray, categories: Strings, permutation=None, segments=None, **kwargs) → Categorical[source]¶

Make a Categorical from codes and categories arrays. If codes and categories have already been pre-computed, this constructor saves time. If not, please use the normal constructor.

Parameters:

codes (pdarray, int64) – Category indices of each value
categories (Strings) – Unique category labels
permutation (pdarray, int64) – The permutation that groups the values in the same order as categories
segments (pdarray, int64) – When values are grouped, the starting offset of each group

Returns:

The Categorical object created from the input parameters

Return type:

Raises:

TypeError – Raised if codes is not a pdarray of int64 objects or if categories is not a Strings object

from_return_msg(rep_msg) → Categorical[source]¶

Create categorical from return message from server

Notes

This is currently only used when reading a Categorical from HDF5 files.

group() → pdarray[source]¶

Return the permutation that groups the array, placing equivalent categories together. All instances of the same category are guaranteed to lie in one contiguous block of the permuted array, but the blocks are not necessarily ordered.

Returns:: The permutation that groups the array by value
Return type:: pdarray

See also

Categorical.save, load_all

Notes

This method is faster than the corresponding Strings method. If the Categorical was created from a Strings object, then this function simply returns the cached permutation. Even if the Categorical was created using from_codes(), this function will be faster than Strings.group() because it sorts dense integer values, rather than 128-bit hash values.

hash() → Tuple[pdarray, pdarray][source]¶

Compute a 128-bit hash of each element of the Categorical.

Returns:: A tuple of two int64 pdarrays. The ith hash value is the concatenation of the ith values from each array.
Return type:: Tuple[pdarray,pdarray]

Notes

The implementation uses SipHash128, a fast and balanced hash function (used by Python for dictionaries and sets). For realistic numbers of strings (up to about 10**15), the probability of a collision between two 128-bit hash values is negligible.

in1d(test: Strings | Categorical) → pdarray[source]¶

Test whether each element of the Categorical object is also present in the test Strings or Categorical object.

Returns a boolean array the same length as self that is True where an element of self is in test and False otherwise.

Parameters:: test (Union[Strings,Categorical]) – The values against which to test each value of ‘self`.
Returns:: The values self[in1d] are in the test Strings or Categorical object.
Return type:: pdarray, bool
Raises:: TypeError – Raised if test is not a Strings or Categorical object

See also

unique, intersect1d, union1d

Notes

in1d can be considered as an element-wise function version of the python keyword in, for 1-D sequences. in1d(a, b) is logically equivalent to ak.array([item in b for item in a]), but is much faster and scales to arbitrarily large a.

Examples

>>> strings = ak.array([f'String {i}' for i in range(0,5)])
>>> cat = ak.Categorical(strings)
>>> ak.in1d(cat,strings)
array([True True True True True])
>>> strings = ak.array([f'String {i}' for i in range(5,9)])
>>> catTwo = ak.Categorical(strings)
>>> ak.in1d(cat,catTwo)
array([False False False False False])

property inferred_type: pdarray¶: Return a string of the type inferred from the values.

info() → str[source]¶

Returns a JSON formatted string containing information about all components of self

Parameters:: None
Returns:: JSON string containing information about all components of self
Return type:: str

is_registered() → np.bool_[source]¶

Return True iff the object is contained in the registry or is a component of a registered object.

Returns:: Indicates if the object is contained in the registry
Return type:: numpy.bool
Raises:: RegistrationError – Raised if there’s a server-side error or a mis-match of registered components

Notes

Objects registered with the server are immune to deletion until they are unregistered.

isna()[source]¶: Find where values are missing or null (as defined by self.NAvalue)

property nbytes¶

The size of the Categorical in bytes.

Returns:: The size of the Categorical in bytes.
Return type:: int

objType(*args, **kwargs)¶

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.__str__() (if defined) or repr(object). encoding defaults to sys.getdefaultencoding(). errors defaults to ‘strict’.

parse_hdf_categoricals(d: Mapping[str, pdarray | Strings]) → Tuple[List[str], Dict[str, Categorical]][source]¶

This function should be used in conjunction with the load_all function which reads hdf5 files and reconstitutes Categorical objects. Categorical objects use a naming convention and HDF5 structure so they can be identified and constructed for the user.

In general you should not call this method directly

Parameters:

d (Dictionary of String to either Pdarray or Strings object)

Returns:

2-Tuple of List of strings containing key names which should be removed and Dictionary of
base name to Categorical object

See also

permutation(*args, **kwargs)¶

pretty_print_info() → None[source]¶

Prints information about all components of self in a human readable format

Parameters:: None
Return type:: None

register(user_defined_name: str) → Categorical[source]¶

Parameters:

user_defined_name (str) – user defined name the Categorical is to be registered under, this will be the root name for underlying components

Returns:

The same Categorical which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different Categoricals with the same name.

Return type:

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Categorical with the user_defined_name

Notes

Objects registered with the server are immune to deletion until they are unregistered.

reset_categories() → Categorical[source]¶

Recompute the category labels, discarding any unused labels. This method is often useful after slicing or indexing a Categorical array, when the resulting array only contains a subset of the original categories. In this case, eliminating unused categories can speed up other operations.

Returns:: A Categorical object generated from the current instance
Return type:: Categorical

save(prefix_path: str, dataset: str = 'categorical_array', file_format: str = 'HDF5', mode: str = 'truncate', file_type: str = 'distribute', compression: str | None = None) → str[source]¶

DEPRECATED Save the Categorical object to HDF5 or Parquet. The result is a collection of HDF5/Parquet files, one file per locale of the arkouda server, where each filename starts with prefix_path and dataset. Each locale saves its chunk of the Strings array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in HDF5 files (must not already exist) :type dataset: str :param file_format: The format to save the file to. :type file_format: str {‘HDF5 | ‘Parquet’} :param mode: By default, truncate (overwrite) output files, if they exist.

If ‘append’, create a new Categorical dataset within existing files.

Parameters:

file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
compression (str (Optional)) – {None | ‘snappy’ | ‘gzip’ | ‘brotli’ | ‘zstd’ | ‘lz4’} The compression type to use when writing. This is only supported for Parquet files and will not be used with HDF5.

Return type:

String message indicating result of save operation

Raises:

ValueError – Raised if the lengths of columns and values differ, or the mode is neither ‘truncate’ nor ‘append’
TypeError – Raised if prefix_path, dataset, or mode is not a str

Notes

Important implementation notes: (1) Strings state is saved as two datasets within an hdf5 group: one for the string characters and one for the segments corresponding to the start of each string, (2) the hdf5 group is named via the dataset parameter.

See also

-, -

segments(*args, **kwargs)¶

set_categories(new_categories, NAvalue=None)[source]¶

Set categories to user-defined values.

Parameters:

new_categories (Strings) – The array of new categories to use. Must be unique.
NAvalue (str scalar) – The value to use to represent missing/null data

Returns:

A new Categorical with the user-defined categories. Old values present in new categories will appear unchanged. Old values not present will be assigned the NA value.

Return type:

sort_values()[source]¶

standardize_categories(arrays, NAvalue='N/A')[source]¶

Standardize an array of Categoricals so that they share the same categories.

Parameters:

arrays (sequence of Categoricals) – The Categoricals to standardize
NAvalue (str scalar) – The value to use to represent missing/null data

Returns:

A list of the original Categoricals remapped to the shared categories.

Return type:

List of Categoricals

startswith(substr: bytes | str_scalars, regex: bool = False) → pdarray[source]¶

Check whether each element starts with the given substring.

Parameters:

substr (Union[bytes, str_scalars]) – The substring to search for
regex (bool) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

True for elements that start with substr, False otherwise

Return type:

pdarray, bool

Raises:

TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

Notes

This method can be significantly faster than the corresponding method on Strings objects, because it searches the unique category labels instead of the full array.

to_hdf(prefix_path, dataset='categorical_array', mode='truncate', file_type='distribute')[source]¶

Save the Categorical to HDF5. The result is a collection of HDF5 files, one file per locale of the arkouda server, where each filename starts with prefix_path.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files will share
dataset (str) – Name prefix for saved data within the HDF5 file
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, add data as a new column to existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale.

Return type:

None

See also

load

to_list() → List[source]¶

Convert the Categorical to a list, transferring data from the arkouda server to Python. This conversion discards category information and produces a list of strings. If the arrays exceeds a built-in size limit, a RuntimeError is raised.

Returns:: A list of strings corresponding to the values in this Categorical
Return type:: list

Notes

The number of bytes in the Categorical cannot exceed ak.client.maxTransferBytes, otherwise a RuntimeError will be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting ak.client.maxTransferBytes to a larger value, but proceed with caution.

to_ndarray() → np.ndarray[source]¶

Convert the array to a np.ndarray, transferring array data from the arkouda server to Python. This conversion discards category information and produces an ndarray of strings. If the arrays exceeds a built-in size limit, a RuntimeError is raised.

Returns:: A numpy ndarray of strings corresponding to the values in this array
Return type:: np.ndarray

Notes

The number of bytes in the array cannot exceed ak.client.maxTransferBytes, otherwise a RuntimeError will be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting ak.client.maxTransferBytes to a larger value, but proceed with caution.

to_pandas() → pd_Categorical[source]¶: Return the equivalent Pandas Categorical.

to_parquet(prefix_path: str, dataset: str = 'categorical_array', mode: str = 'truncate', compression: str | None = None) → str[source]¶

This functionality is currently not supported and will also raise a RuntimeError. Support is in development. Save the Categorical to Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the array to its corresponding file.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in HDF5 files (must not already exist)
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, create a new Categorical dataset within existing files.
compression (str (Optional)) – Default None Provide the compression type to use when writing the file. Supported values: snappy, gzip, brotli, zstd, lz4

Return type:

String message indicating result of save operation

Raises:

RuntimeError – On run due to compatability issues of Categorical with Parquet.

Notes

The prefix_path must be visible to the arkouda server and the user must

have write permission. - Output files have names of the form <prefix_path>_LOCALE, where  ranges from 0 to numLocales for file_type=’distribute’. - ‘append’ write mode is supported, but is not efficient. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, a RuntimeError will result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.

See also

arkouda.dataframe.DataFrameGroupBy or arkouda.groupbyclass.GroupBy

to_strings() → List[source]¶

Convert the Categorical to Strings.

Returns:: A Strings object corresponding to the values in this Categorical.
Return type:: arkouda.numpy.strings.Strings

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> a = ak.array(["a","b","c"])
>>> a
array(['a', 'b', 'c'])
>>> c = ak.Categorical(a)
>>> c.to_strings()
array(['a', 'b', 'c'])

>>> isinstance(c.to_strings(), ak.Strings)
True

transfer(hostname: str, port: int_scalars)[source]¶

Sends a Categorical object to a different Arkouda server

Parameters:

hostname (str) – The hostname where the Arkouda server intended to receive the Categorical is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().

Return type:

A message indicating a complete transfer

Raises:

ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype

unique() → Categorical[source]¶

unregister() → None[source]¶

Unregister this Categorical object in the arkouda server which was previously registered using register() and/or attached to using attach()

Raises:: RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister

Notes

Objects registered with the server are immune to deletion until they are unregistered.

unregister_categorical_by_name(user_defined_name: str) → None[source]¶

Function to unregister Categorical object by name which was registered with the arkouda server via register()

Parameters:

user_defined_name (str) – Name under which the Categorical object was registered

Raises:

TypeError – if user_defined_name is not a string
RegistrationError – if there is an issue attempting to unregister any underlying components

update_hdf(prefix_path, dataset='categorical_array', repack=True)[source]¶

Overwrite the dataset with the name provided with this Categorical object. If the dataset does not exist it is added.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.

Return type:

None

Raises:

RuntimeError – Raised if a server-side error is thrown saving the Categorical

Notes

If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added
Because HDF5 deletes do not release memory, the repack option allows for automatic creation of a file without the inaccessible data.

class arkouda.DType[source]¶

Bases: enum.Enum

Generic enumeration.

Derive from this class to define new enumerations.

BIGINT = 'bigint'¶

BOOL = 'bool'¶

COMPLEX128 = 'complex128'¶

COMPLEX64 = 'complex64'¶

FLOAT = 'float'¶

FLOAT32 = 'float32'¶

FLOAT64 = 'float64'¶

INT = 'int'¶

INT16 = 'int16'¶

INT32 = 'int32'¶

INT64 = 'int64'¶

INT8 = 'int8'¶

STR = 'str'¶

UINT = 'uint'¶

UINT16 = 'uint16'¶

UINT32 = 'uint32'¶

UINT64 = 'uint64'¶

UINT8 = 'uint8'¶

arkouda.DTypeObjects¶

arkouda.DTypes¶

class arkouda.DataFrame(dict=None, /, **kwargs)[source]¶

Bases: collections.UserDict

A DataFrame structure based on arkouda arrays.

Parameters:

initialdata (List or dictionary of lists, tuples, or pdarrays) – Each list/dictionary entry corresponds to one column of the data and should be a homogenous type. Different columns may have different types. If using a dictionary, keys should be strings.
index (Index, pdarray, or Strings) – Index for the resulting frame. Defaults to an integer range.
columns (List, tuple, pdarray, or Strings) – Column labels to use if the data does not include them. Elements must be strings. Defaults to an stringified integer range.

Examples

Create an empty DataFrame and add a column of data:

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame()
>>> df['a'] = ak.array([1,2,3])
>>> display(df)

	a
0	1
1	2
2	3

Create a new DataFrame using a dictionary of data:

>>> userName = ak.array(['Alice', 'Bob', 'Alice', 'Carol', 'Bob', 'Alice'])
>>> userID = ak.array([111, 222, 111, 333, 222, 111])
>>> item = ak.array([0, 0, 1, 1, 2, 0])
>>> day = ak.array([5, 5, 6, 5, 6, 6])
>>> amount = ak.array([0.5, 0.6, 1.1, 1.2, 4.3, 0.6])
>>> df = ak.DataFrame({'userName': userName, 'userID': userID,
>>>            'item': item, 'day': day, 'amount': amount})
>>> display(df)

	userName	userID	item	day	amount
0	Alice	111	0	5	0.5
1	Bob	222	0	5	0.6
2	Alice	111	1	6	1.1
3	Carol	333	1	5	1.2
4	Bob	222	2	6	4.3
5	Alice	111	0	6	0.6

Indexing works slightly differently than with pandas:

>>> df[0]

keys	values
userName	Alice
userID	111
item	0
day	5
amount	0.5

>>> df['userID']
array([111, 222, 111, 333, 222, 111])

>>> df['userName']
array(['Alice', 'Bob', 'Alice', 'Carol', 'Bob', 'Alice'])

>>> df[ak.array([1,3,5])]

	userName	userID	item	day	amount
0	Bob	222	0	5	0.6
1	Carol	333	1	5	1.2
2	Alice	111	0	6	0.6

Compute the stride:

>>> df[1:5:1]

	userName	userID	item	day	amount
0	Bob	222	0	5	0.6
1	Alice	111	1	6	1.1
2	Carol	333	1	5	1.2
3	Bob	222	2	6	4.3

>>> df[ak.array([1,2,3])]

	userName	userID	item	day	amount
0	Bob	222	0	5	0.6
1	Alice	111	1	6	1.1
2	Carol	333	1	5	1.2

>>> df[['userID', 'day']]

	userID	day
0	111	5
1	222	5
2	111	6
3	333	5
4	222	6
5	111	6

GroupBy(keys, use_series=False, as_index=True, dropna=True)[source]¶

Group the dataframe by a column or a list of columns.

Parameters:

keys (str or list of str) – An (ordered) list of column names or a single string to group by.
use_series (bool, default=False) – If True, returns an arkouda.dataframe.DataFrameGroupBy object. Otherwise an arkouda.groupbyclass.GroupBy object.
as_index (bool, default=True) – If True, groupby columns will be set as index otherwise, the groupby columns will be treated as DataFrame columns.
dropna (bool, default=True) – If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.

Returns:

If use_series = True, returns an arkouda.dataframe.DataFrameGroupBy object. Otherwise returns an arkouda.groupbyclass.GroupBy object.

Return type:

See also

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1.0, 1.0, 2.0, np.nan], 'col2': [4, 5, 6, 7]})
>>> df

	col1	col2
0	1	4
1	1	5
2	2	6
3	nan	7

>>> df.GroupBy("col1")
<arkouda.groupbyclass.GroupBy at 0x7f2cf23e10c0>
>>> df.GroupBy("col1").size()
(array([1.00000000000000000 2.00000000000000000]), array([2 1]))

>>> df.GroupBy("col1",use_series=True)
col1
1.0    2
2.0    1
dtype: int64
>>> df.GroupBy("col1",use_series=True, as_index = False).size()

	col1	size
0	1	2
1	2	1

all(axis=0) → Series | bool[source]¶

Return whether all elements are True, potentially over an axis.

Returns True unless there at least one element along a Dataframe axis that is False.

Currently, will ignore any columns that are not type bool. This is equivalent to the pandas option bool_only=True.

Parameters:

axis ({0 or ‘index’, 1 or ‘columns’, None}, default = 0) –

Indicate which axis or axes should be reduced.

0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.

1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.

None : reduce all axes, return a scalar.

Return type:

Raises:

ValueError – Raised if axis does not have a value in {0 or ‘index’, 1 or ‘columns’, None}.

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({"A":[True,True,True,False],"B":[True,True,True,False],
...          "C":[True,False,True,False],"D":[True,True,True,True]})

	A	B	C	D
0	True	True	True	True
1	True	True	False	True
2	True	True	True	True
3	False	False	False	True

>>> df.all(axis=0)
A    False
B    False
C    False
D     True
dtype: bool
>>> df.all(axis=1)
0     True
1    False
2     True
3    False
dtype: bool
>>> df.all(axis=None)
False

any(axis=0) → Series | bool[source]¶

Return whether any element is True, potentially over an axis.

Returns False unless there is at least one element along a Dataframe axis that is True.

Currently, will ignore any columns that are not type bool. This is equivalent to the pandas option bool_only=True.

Parameters:

axis ({0 or ‘index’, 1 or ‘columns’, None}, default = 0) –

Indicate which axis or axes should be reduced.

0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.

1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.

None : reduce all axes, return a scalar.

Return type:

Raises:

ValueError – Raised if axis does not have a value in {0 or ‘index’, 1 or ‘columns’, None}.

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({"A":[True,True,True,False],"B":[True,True,True,False],
...          "C":[True,False,True,False],"D":[False,False,False,False]})

	A	B	C	D
0	True	True	True	False
1	True	True	False	False
2	True	True	True	False
3	False	False	False	False

>>> df.any(axis=0)
A     True
B     True
C     True
D    False
dtype: bool
>>> df.any(axis=1)
0     True
1     True
2     True
3    False
dtype: bool
>>> df.any(axis=None)
True

append(other, ordered=True)[source]¶

Concatenate data from ‘other’ onto the end of this DataFrame, in place.

Explicitly, use the arkouda concatenate function to append the data from each column in other to the end of self. This operation is done in place, in the sense that the underlying pdarrays are updated from the result of the arkouda concatenate function, rather than returning a new DataFrame object containing the result.

Parameters:

other (DataFrame) – The DataFrame object whose data will be appended to this DataFrame.
ordered (bool, default=True) – If False, allow rows to be interleaved for better performance (but data within a row remains together). By default, append all rows to the end, in input order.

Returns:

Appending occurs in-place, but result is returned for compatibility.

Return type:

self

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df1 = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]})

	col1	col2
0	1	3
1	2	4

>>> df2 = ak.DataFrame({'col1': [3], 'col2': [5]})

	col1	col2
0	3	5

>>> df1.append(df2)
>>> df1

	col1	col2
0	1	3
1	2	4
2	3	5

apply_permutation(perm)[source]¶

Apply a permutation to an entire DataFrame. The operation is done in place and the original DataFrame will be modified.

This may be useful if you want to unsort an DataFrame, or even to apply an arbitrary permutation such as the inverse of a sorting permutation.

Parameters:: perm (pdarray) – A permutation array. Should be the same size as the data arrays, and should consist of the integers [0,size-1] in some order. Very minimal testing is done to ensure this is a permutation.
Return type:: None

See also

sort

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})

	col1	col2
0	1	4
1	2	5
2	3	6

>>> perm_arry = ak.array([0, 2, 1])
>>> df.apply_permutation(perm_arry)
>>> display(df)

	col1	col2
0	1	4
1	3	6
2	2	5

argsort(key, ascending=True)[source]¶

Return the permutation that sorts the dataframe by key.

Parameters:

key (str) – The key to sort on.
ascending (bool, default = True) – If true, sort the key in ascending order. Otherwise, sort the key in descending order.

Returns:

The permutation array that sorts the data on key.

Return type:

See also

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1.1, 3.1, 2.1], 'col2': [6, 5, 4]})
>>> display(df)

	col1	col2
0	1.1	6
1	3.1	5
2	2.1	4

>>> df.argsort('col1')
array([0 2 1])
>>> sorted_df1 = df[df.argsort('col1')]
>>> display(sorted_df1)

	col1	col2
0	1.1	6
1	2.1	4
2	3.1	5

>>> df.argsort('col2')
array([2 1 0])
>>> sorted_df2 = df[df.argsort('col2')]
>>> display(sorted_df2)

	col1	col2
0	2.1	4
1	3.1	5
2	1.1	6

assign(**kwargs) → DataFrame[source]¶

Assign new columns to a DataFrame.

Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.

Parameters:: **kwargs (dict of {str: callable or Series}) – The column names are keywords. If the values are callable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn’t check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.
Returns:: A new DataFrame with the new columns in addition to all the existing columns.
Return type:: DataFrame

Notes

Assigning multiple columns within the same assign is possible. Later items in ‘**kwargs’ may refer to newly created or modified columns in ‘df’; items are computed and assigned into ‘df’ in order.

Examples

>>> df = ak.DataFrame({'temp_c': [17.0, 25.0]},
...                   index=['Portland', 'Berkeley'])
>>> df
          temp_c
Portland    17.0
Berkeley    25.0

Where the value is a callable, evaluated on df:

>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

Alternatively, the same behavior can be achieved by directly referencing an existing Series or sequence:

>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

You can create multiple columns within the same assign where one of the columns depends on another one defined within the same assign:

>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
...           temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9)
          temp_c  temp_f  temp_k
Portland    17.0    62.6  290.15
Berkeley    25.0    77.0  298.15

attach(user_defined_name: str) → DataFrame[source]¶

Function to return a DataFrame object attached to the registered name in the arkouda server which was registered using register().

Parameters:: user_defined_name (str) – user defined name which DataFrame object was registered under.
Returns:: The DataFrame object created by re-attaching to the corresponding server components.
Return type:: arkouda.dataframe.DataFrame
Raises:: RegistrationError – if user_defined_name is not registered

Example

>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
>>> df.register("my_table_name")
>>> df.attach("my_table_name")
>>> df.is_registered()
True
>>> df.unregister()
>>> df.is_registered()
False

coargsort(keys, ascending=True)[source]¶

Return the permutation that sorts the dataframe by keys.

Note: Sorting using Strings may not yield correct sort order.

Parameters:: keys (list of str) – The keys to sort on.
Returns:: The permutation array that sorts the data on keys.
Return type:: arkouda.numpy.pdarrayclass.pdarray

Example

>>> df = ak.DataFrame({'col1': [2, 2, 1], 'col2': [3, 4, 3], 'col3':[5, 6, 7]})
>>> display(df)

	col1	col2	col3
0	2	3	5
1	2	4	6
2	1	3	7

>>> df.coargsort(['col1', 'col2'])
array([2 0 1])
>>>

property columns¶

An Index where the values are the column names of the dataframe.

Returns:: The values of the index are the column names of the dataframe.
Return type:: arkouda.index.Index

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df

	col1	col2
0	1	3
1	2	4

>>> df.columns
Index(array(['col1', 'col2']), dtype='<U0')

concat(items, ordered=True)[source]¶: Essentially an append, but different formatting.

corr() → DataFrame[source]¶

Return new DataFrame with pairwise correlation of columns.

Returns:: Arkouda DataFrame containing correlation matrix of all columns.
Return type:: arkouda.dataframe.DataFrame
Raises:: RuntimeError – Raised if there’s a server-side error thrown.

See also

pdarray.corr

Notes

Generates the correlation matrix using Pearson R for all columns.

Attempts to convert to numeric values where possible for inclusion in the matrix.

Example

>>> df = ak.DataFrame({'col1': [1, 2], 'col2': [-1, -2]})
>>> display(df)

	col1	col2
0	1	-1
1	2	-2

>>> corr = df.corr()

	col1	col2
col1	1	-1
col2	-1	1

count(axis: int | str = 0, numeric_only=False) → Series[source]¶

Count non-NA cells for each column or row.

The values np.NaN are considered NA.

Parameters:

axis ({0 or 'index', 1 or 'columns'}, default 0) – If 0 or ‘index’ counts are generated for each column. If 1 or ‘columns’ counts are generated for each row.
numeric_only (bool = False) – Include only float, int or boolean data.

Returns:

For each column/row the number of non-NA/null entries.

Return type:

arkouda.series.Series

Raises:

ValueError – Raised if axis is not 0, 1, ‘index’, or ‘columns’.

See also

GroupBy.count

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import numpy as np
>>> df = ak.DataFrame({'col_A': ak.array([7, np.nan]), 'col_B':ak.array([1, 9])})
>>> display(df)

	col_A	col_B
0	7	1
1	nan	9

>>> df.count()
col_A    1
col_B    2
dtype: int64

>>> df = ak.DataFrame({'col_A': ak.array(["a","b","c"]), 'col_B':ak.array([1, np.nan, np.nan])})
>>> display(df)

	col_A	col_B
0	a	1
1	b	nan
2	c	nan

>>> df.count()
col_A    3
col_B    1
dtype: int64

>>> df.count(numeric_only=True)
col_B    1
dtype: int64

>>> df.count(axis=1)
0    2
1    1
2    1
dtype: int64

Drop column/s or row/s from the dataframe.

Parameters:

keys (str, int or list) – The labels to be dropped on the given axis.
axis (int or str) – The axis on which to drop from. 0/’index’ - drop rows, 1/’columns’ - drop columns.
inplace (bool, default=False) – When True, perform the operation on the calling object. When False, return a new object.

Returns:

DateFrame when inplace=False; None when inplace=True

Return type:

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> display(df)

	col1	col2
0	1	3
1	2	4

Drop column

>>> df.drop('col1', axis = 1)

	col2
0	3
1	4

Drop row

>>> df.drop(0, axis = 0)

	col1	col2
0	2	4

drop_duplicates(subset=None, keep='first')[source]¶

Drops duplcated rows and returns resulting DataFrame.

If a subset of the columns are provided then only one instance of each duplicated row will be returned (keep determines which row).

Parameters:

subset (Iterable) – Iterable of column names to use to dedupe.
keep ({'first', 'last'}, default='first') – Determines which duplicates (if any) to keep.

Returns:

DataFrame with duplicates removed.

Return type:

Example

>>> df = ak.DataFrame({'col1': [1, 2, 2, 3], 'col2': [4, 5, 5, 6]})
>>> display(df)

	col1	col2
0	1	4
1	2	5
2	2	5
3	3	6

>>> df.drop_duplicates()

	col1	col2
0	1	4
1	2	5
2	3	6

dropna(axis: int | str = 0, how: str | None = None, thresh: int | None = None, ignore_index: bool = False) → DataFrame[source]¶

Remove missing values.

Parameters:

axis ({0 or 'index', 1 or 'columns'}, default = 0) –
Determine if rows or columns which contain missing values are removed.

0, or ‘index’: Drop rows which contain missing values.

1, or ‘columns’: Drop columns which contain missing value.

Only a single axis is allowed.
how ({'any', 'all'}, default='any') –
Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.

’any’: If any NA values are present, drop that row or column.

’all’: If all values are NA, drop that row or column.
thresh (int, optional) – Require that many non - NA values.Cannot be combined with how.
ignore_index (bool, default False) – If True, the resulting axis will be labeled 0, 1, …, n - 1.

Returns:

DataFrame with NA entries dropped from it.

Return type:

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import numpy as np
>>> df = ak.DataFrame(
    {
        "A": [True, True, True, True],
        "B": [1, np.nan, 2, np.nan],
        "C": [1, 2, 3, np.nan],
        "D": [False, False, False, False],
        "E": [1, 2, 3, 4],
        "F": ["a", "b", "c", "d"],
        "G": [1, 2, 3, 4],
    }
   )

>>> display(df)

	A	B	C	D	E	F	G
0	True	1	1	False	1	a	1
1	True	nan	2	False	2	b	2
2	True	2	3	False	3	c	3
3	True	nan	nan	False	4	d	4

>>> df.dropna()

	A	B	C	D	E	F	G
0	True	1	1	False	1	a	1
1	True	2	3	False	3	c	3

>>> df.dropna(axis=1)

	A	D	E	F	G
0	True	False	1	a	1
1	True	False	2	b	2
2	True	False	3	c	3
3	True	False	4	d	4

>>> df.dropna(axis=1, thresh=3)

	A	C	D	E	F	G
0	True	1	False	1	a	1
1	True	2	False	2	b	2
2	True	3	False	3	c	3
3	True	nan	False	4	d	4

>>> df.dropna(axis=1, how="all")

	A	B	C	D	E	F	G
0	True	1	1	False	1	a	1
1	True	nan	2	False	2	b	2
2	True	2	3	False	3	c	3
3	True	nan	nan	False	4	d	4

property dtypes: DataFrame¶

The dtypes of the dataframe.

Returns:: dtypes – The dtypes of the dataframe.
Return type:: arkouda.row.Row

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1, 2], 'col2': ["a", "b"]})
>>> df

	col1	col2
0	1	a
1	2	b

>>> df.dtypes

keys	values
col1	int64
col2	str

property empty: DataFrame¶

Whether the dataframe is empty.

Returns:: True if the dataframe is empty, otherwise False.
Return type:: bool

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({})
>>> df
 0 rows x 0 columns
>>> df.empty
True

filter_by_range(keys, low=1, high=None)[source]¶

Find all rows where the value count of the items in a given set of columns (keys) is within the range [low, high].

To filter by a specific value, set low == high.

Parameters:

keys (str or list of str) – The names of the columns to group by.
low (int, default=1) – The lowest value count.
high (int, default=None) – The highest value count, default to unlimited.

Returns:

An array of boolean values for qualified rows in this DataFrame.

Return type:

arkouda.dataframe.DataFrameGroupBy or arkouda.groupbyclass.GroupBy

Example

>>> df = ak.DataFrame({'col1': [1, 2, 2, 2, 3, 3], 'col2': [4, 5, 6, 7, 8, 9]})
>>> display(df)

	col1	col2
0	1	4
1	2	5
2	2	6
3	2	7
4	3	8
5	3	9

>>> df.filter_by_range("col1", low=1, high=2)
array([True False False False True True])

>>> filtered_df = df[df.filter_by_range("col1", low=1, high=2)]
>>> display(filtered_df)

	col1	col2
0	1	4
1	3	8
2	3	9

from_pandas(pd_df)[source]¶

Copy the data from a pandas DataFrame into a new arkouda.dataframe.DataFrame.

Parameters:: pd_df (pandas.DataFrame) – A pandas DataFrame to convert.
Return type:: arkouda.dataframe.DataFrame

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import pandas as pd
>>> pd_df = pd.DataFrame({"A":[1,2],"B":[3,4]})
>>> type(pd_df)
pandas.core.frame.DataFrame
>>> display(pd_df)

	A	B
0	1	3
1	2	4

>>> ak_df = DataFrame.from_pandas(pd_df)
>>> type(ak_df)
arkouda.dataframe.DataFrame
>>> display(ak_df)

	A	B
0	1	3
1	2	4

from_return_msg(rep_msg)[source]¶

Creates a DataFrame object from an arkouda server response message.

Parameters:: rep_msg (string) – Server response message used to create a DataFrame.
Return type:: arkouda.dataframe.DataFrame

groupby(keys, use_series=True, as_index=True, dropna=True)[source]¶

Group the dataframe by a column or a list of columns. Alias for GroupBy.

Parameters:

keys (str or list of str) – An (ordered) list of column names or a single string to group by.
use_series (bool, default=True) – If True, returns an arkouda.dataframe.DataFrameGroupBy object. Otherwise an arkouda.groupbyclass.GroupBy object.
as_index (bool, default=True) – If True, groupby columns will be set as index otherwise, the groupby columns will be treated as DataFrame columns.
dropna (bool, default=True) – If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.

Returns:

If use_series = True, returns an arkouda.dataframe.DataFrameGroupBy object. Otherwise returns an arkouda.groupbyclass.GroupBy object.

Return type:

See also

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1.0, 1.0, 2.0, np.nan], 'col2': [4, 5, 6, 7]})
>>> df

	col1	col2
0	1	4
1	1	5
2	2	6
3	nan	7

>>> df.GroupBy("col1")
<arkouda.groupbyclass.GroupBy at 0x7f2cf23e10c0>
>>> df.GroupBy("col1").size()
(array([1.00000000000000000 2.00000000000000000]), array([2 1]))

>>> df.GroupBy("col1",use_series=True)
col1
1.0    2
2.0    1
dtype: int64
>>> df.GroupBy("col1",use_series=True, as_index = False).size()

	col1	size
0	1	2
1	2	1

head(n=5)[source]¶

Return the first n rows.

This function returns the first n rows of the the dataframe. It is useful for quickly verifying data, for example, after sorting or appending rows.

Parameters:: n (int, default = 5) – Number of rows to select.
Returns:: The first n rows of the DataFrame.
Return type:: arkouda.dataframe.DataFrame

See also

tail

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': ak.arange(10), 'col2': -1 * ak.arange(10)})
>>> display(df)

	col1	col2
0	0	0
1	1	-1
2	2	-2
3	3	-3
4	4	-4
5	5	-5
6	6	-6
7	7	-7
8	8	-8
9	9	-9

>>> df.head()

	col1	col2
0	0	0
1	1	-1
2	2	-2
3	3	-3
4	4	-4

>>> df.head(n=2)

	col1	col2
0	0	0
1	1	-1

property index¶

The index of the dataframe.

Returns:: The index of the dataframe.
Return type:: arkouda.index.Index or arkouda.index.MultiIndex

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df

	col1	col2
0	1	3
1	2	4

>>> df.index
Index(array([0 1]), dtype='int64')

property info¶

Returns a summary string of this dataframe.

Returns:: A summary string of this dataframe.
Return type:: str

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1, 2], 'col2': ["a", "b"]})
>>> df

	col1	col2
0	1	a
1	2	b

>>> df.info
"DataFrame(['col1', 'col2'], 2 rows, 20 B)"

is_registered() → bool[source]¶

Return True if the object is contained in the registry.

Returns:: Indicates if the object is contained in the registry.
Return type:: bool
Raises:: RegistrationError – Raised if there’s a server-side error or a mismatch of registered components.

Notes

Objects registered with the server are immune to deletion until they are unregistered.

Example

>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
>>> df.register("my_table_name")
>>> df.attach("my_table_name")
>>> df.is_registered()
True
>>> df.unregister()
>>> df.is_registered()
False

isin(values: pdarray | Dict | Series | DataFrame) → DataFrame[source]¶

Determine whether each element in the DataFrame is contained in values.

Parameters:: values (pdarray, dict, Series, or DataFrame) – The values to check for in DataFrame. Series can only have a single index.
Returns:: Arkouda DataFrame of booleans showing whether each element in the DataFrame is contained in values.
Return type:: arkouda.dataframe.DataFrame

See also

ak.Series.isin

Notes

Pandas supports values being an iterable type. In arkouda, we replace this with pdarray.
Pandas supports ~ operations. Currently, ak.DataFrame does not support this.

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col_A': ak.array([7, 3]), 'col_B':ak.array([1, 9])})
>>> display(df)

	col_A	col_B
0	7	1
1	3	9

When values is a pdarray, check every value in the DataFrame to determine if it exists in values.

>>> df.isin(ak.array([0, 1]))

	col_A	col_B
0	0	1
1	0	0

When values is a dict, the values in the dict are passed to check the column indicated by the key.

>>> df.isin({'col_A': ak.array([0, 3])})

	col_A	col_B
0	0	0
1	1	0

When values is a Series, each column is checked if values is present positionally. This means that for True to be returned, the indexes must be the same.

>>> i = ak.Index(ak.arange(2))
>>> s = ak.Series(data=[3, 9], index=i)
>>> df.isin(s)

	col_A	col_B
0	0	0
1	0	1

When values is a DataFrame, the index and column must match. Note that 9 is not found because the column name does not match.

>>> other_df = ak.DataFrame({'col_A':ak.array([7, 3]), 'col_C':ak.array([0, 9])})
>>> df.isin(other_df)

	col_A	col_B
0	1	0
1	1	0

isna() → DataFrame[source]¶

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. numpy.NaN values get mapped to True values. Everything else gets mapped to False values.

Returns:: Mask of bool values for each element in DataFrame that indicates whether an element is an NA value.
Return type:: arkouda.dataframe.DataFrame

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import numpy as np
>>> df = ak.DataFrame({"A": [np.nan, 2, 2, 3], "B": [3, np.nan, 5, 6],
...          "C": [1, np.nan, 2, np.nan], "D":["a","b","c","d"]})
>>> display(df)

	A	B	C	D
0	nan	3	1	a
1	2	nan	nan	b
2	2	5	2	c
3	3	6	nan	d

>>> df.isna()
       A      B      C      D
0   True  False  False  False
1  False   True   True  False
2  False  False  False  False
3  False  False   True  False (4 rows x 4 columns)

load(prefix_path, file_format='INFER')[source]¶

Load dataframe from file. file_format needed for consistency with other load functions.

Parameters:

prefix_path (str) – The prefix path for the data.
file_format (string, default = "INFER")

Returns:

A dataframe loaded from the prefix_path.

Return type:

Examples

To store data in <my_dir>/my_data_LOCALE0000, use “<my_dir>/my_data” as the prefix.

>>> import arkouda as ak
>>> ak.connect()
>>> import os.path
>>> from pathlib import Path
>>> my_path = os.path.join(os.getcwd(), 'hdf5_output','my_data')
>>> Path(my_path).mkdir(parents=True, exist_ok=True)
>>> df = ak.DataFrame({"A": ak.arange(5), "B": -1 * ak.arange(5)})
>>> df.save(my_path, file_type="distribute")
>>> df.load(my_path)

	A	B
0	0	0
1	1	-1
2	2	-2
3	3	-3
4	4	-4

memory_usage(index=True, unit='B') → Series[source]¶

Return the memory usage of each column in bytes.

The memory usage can optionally include the contribution of the index.

Parameters:

index (bool, default True) – Specifies whether to include the memory usage of the DataFrame’s index in returned Series. If index=True, the memory usage of the index is the first item in the output.
unit (str, default = "B") – Unit to return. One of {‘B’, ‘KB’, ‘MB’, ‘GB’}.

Returns:

A Series whose index is the original column names and whose values is the memory usage of each column in bytes.

Return type:

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> dtypes = [ak.int64, ak.float64,  ak.bool]
>>> data = dict([(str(t), ak.ones(5000, dtype=ak.int64).astype(t)) for t in dtypes])
>>> df = ak.DataFrame(data)
>>> display(df.head())

	int64	float64	bool
0	1	1	True
1	1	1	True
2	1	1	True
3	1	1	True
4	1	1	True

>>> df.memory_usage()

	0
Index	40000
int64	40000
float64	40000
bool	5000

>>> df.memory_usage(index=False)

	0
int64	40000
float64	40000
bool	5000

>>> df.memory_usage(unit="KB")

	0
Index	39.0625
int64	39.0625
float64	39.0625
bool	4.88281

To get the approximate total memory usage:

>>>  df.memory_usage(index=True).sum()

memory_usage_info(unit='GB')[source]¶

A formatted string representation of the size of this DataFrame.

Parameters:: unit (str, default = "GB") – Unit to return. One of {‘KB’, ‘MB’, ‘GB’}.
Returns:: A string representation of the number of bytes used by this DataFrame in [unit]s.
Return type:: str

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': ak.arange(1000), 'col2': ak.arange(1000)})
>>> df.memory_usage_info()
'0.00 GB'

>>> df.memory_usage_info(unit="KB")
'15 KB'

merge(right: DataFrame, on: str | List[str] | None = None, how: str = 'inner', left_suffix: str = '_x', right_suffix: str = '_y', convert_ints: bool = True, sort: bool = True) → DataFrame[source]¶

Merge Arkouda DataFrames with a database-style join. The resulting dataframe contains rows from both DataFrames as specified by the merge condition (based on the “how” and “on” parameters).

Based on pandas merge functionality. https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html

Parameters:

right (DataFrame) – The Right DataFrame to be joined.
on (Optional[Union[str, List[str]]] = None) – The name or list of names of the DataFrame column(s) to join on. If on is None, this defaults to the intersection of the columns in both DataFrames.
how ({"inner", "left", "right}, default = "inner") – The merge condition. Must be “inner”, “left”, or “right”.
left_suffix (str, default = "_x") – A string indicating the suffix to add to columns from the left dataframe for overlapping column names in both left and right. Defaults to “_x”. Only used when how is “inner”.
right_suffix (str, default = "_y") – A string indicating the suffix to add to columns from the right dataframe for overlapping column names in both left and right. Defaults to “_y”. Only used when how is “inner”.
convert_ints (bool = True) – If True, convert columns with missing int values (due to the join) to float64. This is to match pandas. If False, do not convert the column dtypes. This has no effect when how = “inner”.
sort (bool = True) – If True, DataFrame is returned sorted by “on”. Otherwise, the DataFrame is not sorted.

Returns:

Joined Arkouda DataFrame.

Return type:

Note

Multiple column joins are only supported for integer columns.

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> left_df = ak.DataFrame({'col1': ak.arange(5), 'col2': -1 * ak.arange(5)})
>>> display(left_df)

	col1	col2
0	0	0
1	1	-1
2	2	-2
3	3	-3
4	4	-4

>>> right_df = ak.DataFrame({'col1': 2 * ak.arange(5), 'col2': 2 * ak.arange(5)})
>>> display(right_df)

	col1	col2
0	0	0
1	2	2
2	4	4
3	6	6
4	8	8

>>> left_df.merge(right_df, on = "col1")

	col1	col2_x	col2_y
0	0	0	0
1	2	-2	2
2	4	-4	4

>>> left_df.merge(right_df, on = "col1", how = "left")

	col1	col2_y	col2_x
0	0	0	0
1	1	nan	-1
2	2	2	-2
3	3	nan	-3
4	4	4	-4

>>> left_df.merge(right_df, on = "col1", how = "right")

	col1	col2_x	col2_y
0	0	0	0
1	2	-2	2
2	4	-4	4
3	6	nan	6
4	8	nan	8

>>> left_df.merge(right_df, on = "col1", how = "outer")

	col1	col2_y	col2_x
0	0	0	0
1	1	nan	-1
2	2	2	-2
3	3	nan	-3
4	4	4	-4
5	6	6	nan
6	8	8	nan

notna() → DataFrame[source]¶

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. numpy.NaN values get mapped to False values.

Returns:: Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.
Return type:: arkouda.dataframe.DataFrame

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import numpy as np
>>> df = ak.DataFrame({"A": [np.nan, 2, 2, 3], "B": [3, np.nan, 5, 6],
...          "C": [1, np.nan, 2, np.nan], "D":["a","b","c","d"]})
>>> display(df)

	A	B	C	D
0	nan	3	1	a
1	2	nan	nan	b
2	2	5	2	c
3	3	6	nan	d

>>> df.notna()
       A      B      C     D
0  False   True   True  True
1   True  False  False  True
2   True   True   True  True
3   True   True  False  True (4 rows x 4 columns)

objType(*args, **kwargs)¶

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

read_csv(filename: str, col_delim: str = ',')[source]¶

Read the columns of a CSV file into an Arkouda DataFrame. If the file contains the appropriately formatted header, typed data will be returned. Otherwise, all data will be returned as a Strings objects.

Parameters:

filename (str) – Filename to read data from.
col_delim (str, default=",") – The delimiter for columns within the data.

Returns:

Arkouda DataFrame containing the columns from the CSV file.

Return type:

Raises:

ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist.
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server.

See also

to_csv

Notes

CSV format is not currently supported by load/load_all operations.
The column delimiter is expected to be the same for column names and data.
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (”\n”) at this time.
Unlike other file formats, CSV files store Strings as their UTF-8 format instead of storing

bytes as uint(8).

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import os.path
>>> from pathlib import Path
>>> my_path = os.path.join(os.getcwd(), 'csv_output','my_data')
>>> Path(my_path).mkdir(parents=True, exist_ok=True)

>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]})
>>> df.to_csv(my_path)
>>> df2 = DataFrame.read_csv(my_path + "_LOCALE0000")
>>> display(df2)

	A	B
0	1	3
1	2	4

register(user_defined_name: str) → DataFrame[source]¶

Parameters:

user_defined_name (str) – User defined name the DataFrame is to be registered under. This will be the root name for underlying components.

Returns:

The same DataFrame which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different DataFrames with the same name.

Return type:

Raises:

TypeError – Raised if user_defined_name is not a str.
RegistrationError – If the server was unable to register the DataFrame with the user_defined_name.

Notes

Objects registered with the server are immune to deletion until they are unregistered.

Any changes made to a DataFrame object after registering with the server may not be reflected in attached copies.

Example

>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
>>> df.register("my_table_name")
>>> df.attach("my_table_name")
>>> df.is_registered()
True
>>> df.unregister()
>>> df.is_registered()
False

Rename indexes or columns according to a mapping.

Parameters:

mapper (callable or dict-like, Optional) – Function or dictionary mapping existing values to new values. Nonexistent names will not raise an error. Uses the value of axis to determine if renaming column or index
column (callable or dict-like, Optional) – Function or dictionary mapping existing column names to new column names. Nonexistent names will not raise an error. When this is set, axis is ignored.
index (callable or dict-like, Optional) – Function or dictionary mapping existing index names to new index names. Nonexistent names will not raise an error. When this is set, axis is ignored.
axis (int or str, default=0) – Indicates which axis to perform the rename. 0/”index” - Indexes 1/”column” - Columns
inplace (bool, default=False) – When True, perform the operation on the calling object. When False, return a new object.

Returns:

DateFrame when inplace=False; None when inplace=True.

Return type:

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({"A": ak.array([1, 2, 3]), "B": ak.array([4, 5, 6])})
>>> display(df)

	A	B
0	1	4
1	2	5
2	3	6

Rename columns using a mapping:

>>> df.rename(column={'A':'a', 'B':'c'})

	a	c
0	1	4
1	2	5
2	3	6

Rename indexes using a mapping:

>>> df.rename(index={0:99, 2:11})

	A	B
0	1	4
1	2	5
2	3	6

Rename using an axis style parameter:

>>> df.rename(str.lower, axis='column')

	a	b
0	1	4
1	2	5
2	3	6

reset_index(size: int | None = None, inplace: bool = False) → None | DataFrame[source]¶

Set the index to an integer range.

Useful if this dataframe is the result of a slice operation from another dataframe, or if you have permuted the rows and no longer need to keep that ordering on the rows.

Parameters:

size (int, optional) – If size is passed, do not attempt to determine size based on existing column sizes. Assume caller handles consistency correctly.
inplace (bool, default=False) – When True, perform the operation on the calling object. When False, return a new object.

Returns:

DateFrame when inplace=False; None when inplace=True.

Return type:

arkouda.dataframe.DataFrameGroupBy or arkouda.groupbyclass.GroupBy

Note

Pandas adds a column ‘index’ to indicate the original index. Arkouda does not currently support this behavior.

Example

>>> df = ak.DataFrame({"A": ak.array([1, 2, 3]), "B": ak.array([4, 5, 6])})
>>> display(df)

	A	B
0	1	4
1	2	5
2	3	6

>>> perm_df = df[ak.array([0,2,1])]
>>> display(perm_df)

	A	B
0	1	4
1	3	6
2	2	5

>>> perm_df.reset_index()

	A	B
0	1	4
1	3	6
2	2	5

sample(n=5)[source]¶

Return a random sample of n rows.

Parameters:: n (int, default=5) – Number of rows to return.
Returns:: The sampled n rows of the DataFrame.
Return type:: arkouda.dataframe.DataFrame

Example

>>> df = ak.DataFrame({"A": ak.arange(5), "B": -1 * ak.arange(5)})
>>> display(df)

	A	B
0	0	0
1	1	-1
2	2	-2
3	3	-3
4	4	-4

Random output of size 3:

>>> df.sample(n=3)

	A	B
0	0	0
1	1	-1
2	4	-4

save(path, index=False, columns=None, file_format='HDF5', file_type='distribute', compression: str | None = None)[source]¶

DEPRECATED Save DataFrame to disk, preserving column names.

Parameters:

path (str) – File path to save data.
index (bool, default=False) – If True, save the index column. By default, do not save the index.
columns (list, default=None) – List of columns to include in the file. If None, writes out all columns.
file_format (str, default='HDF5') – ‘HDF5’ or ‘Parquet’. Defaults to ‘HDF5’
file_type (str, default=distribute) – “single” or “distribute” If single, will right a single file to locale 0.
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Compression type. Only used for Parquet

Notes

This method saves one file per locale of the arkouda server. All files are prefixed by the path argument and suffixed by their locale number.

See also

to_parquet, to_hdf

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import os.path
>>> from pathlib import Path
>>> my_path = os.path.join(os.getcwd(), 'hdf5_output')
>>> Path(my_path).mkdir(parents=True, exist_ok=True)

>>> df = ak.DataFrame({"A": ak.arange(5), "B": -1 * ak.arange(5)})
>>> df.save(my_path + '/my_data', file_type="single")
>>> df.load(my_path + '/my_data')

	A	B
0	0	0
1	1	-1
2	2	-2
3	3	-3
4	4	-4

property shape¶

The shape of the dataframe.

Returns:: Tuple of array dimensions.
Return type:: tuple of int

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
>>> df

	col1	col2
0	1	4
1	2	5
2	3	6

>>> df.shape
(3, 2)

property size¶

Returns the number of bytes on the arkouda server.

Returns:: The number of bytes on the arkouda server.
Return type:: int

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
>>> df

	col1	col2
0	1	4
1	2	5
2	3	6

>>> df.size
6

sort_index(ascending=True)[source]¶

Sort the DataFrame by indexed columns.

Note: Fails on sort order of arkouda.numpy.strings.Strings columns when: multiple columns being sorted.

Parameters:: ascending (bool, default = True) – Sort values in ascending (default) or descending order.

Example

>>> df = ak.DataFrame({'col1': [1.1, 3.1, 2.1], 'col2': [6, 5, 4]},
...          index = Index(ak.array([2,0,1]), name="idx"))

>>> display(df)

idx	col1	col2
0	1.1	6
1	3.1	5
2	2.1	4

>>> df.sort_index()

idx	col1	col2
0	3.1	5
1	2.1	4
2	1.1	6

sort_values(by=None, ascending=True)[source]¶

Sort the DataFrame by one or more columns.

If no column is specified, all columns are used.

Note: Fails on order of arkouda.numpy.strings.Strings columns when multiple columns being sorted.

Parameters:

by (str or list/tuple of str, default = None) – The name(s) of the column(s) to sort by.
ascending (bool, default = True) – Sort values in ascending (default) or descending order.

See also

apply_permutation

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [2, 2, 1], 'col2': [3, 4, 3], 'col3':[5, 6, 7]})
>>> display(df)

	col1	col2	col3
0	2	3	5
1	2	4	6
2	1	3	7

>>> df.sort_values()

	col1	col2	col3
0	1	3	7
1	2	3	5
2	2	4	6

>>> df.sort_values("col3")

	col1	col2	col3
0	1	3	7
1	2	3	5
2	2	4	6

tail(n=5)[source]¶

Return the last n rows.

This function returns the last n rows for the dataframe. It is useful for quickly testing if your object has the right type of data in it.

Parameters:: n (int, default=5) – Number of rows to select.
Returns:: The last n rows of the DataFrame.
Return type:: arkouda.dataframe.DataFrame

See also

arkouda.dataframe.head

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': ak.arange(10), 'col2': -1 * ak.arange(10)})
>>> display(df)

	col1	col2
0	0	0
1	1	-1
2	2	-2
3	3	-3
4	4	-4
5	5	-5
6	6	-6
7	7	-7
8	8	-8
9	9	-9

>>> df.tail()

	col1	col2
0	5	-5
1	6	-6
2	7	-7
3	8	-8
4	9	-9

>>> df.tail(n=2)

	col1	col2
0	8	-8
1	9	-9

to_csv(path: str, index: bool = False, columns: List[str] | None = None, col_delim: str = ',', overwrite: bool = False)[source]¶

Writes DataFrame to CSV file(s). File will contain a column for each column in the DataFrame. All CSV Files written by Arkouda include a header denoting data types of the columns. Unlike other file formats, CSV files store Strings as their UTF-8 format instead of storing bytes as uint(8).

Parameters:

path (str) – The filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
index (bool, default=False) – If True, the index of the DataFrame will be written to the file as a column.
columns (list of str (Optional)) – Column names to assign when writing data.
col_delim (str, default=",") – Value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
overwrite (bool, default=False) – If True, any existing files matching your provided prefix_path will be overwritten. If False, an error will be returned if existing files are found.

Return type:

None

Raises:

ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist.
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server.

Notes

CSV format is not currently supported by load/load_all operations.
The column delimiter is expected to be the same for column names and data.
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (”\n”) at this time.

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import os.path
>>> from pathlib import Path
>>> my_path = os.path.join(os.getcwd(), 'csv_output')
>>> Path(my_path).mkdir(parents=True, exist_ok=True)

>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]})
>>> df.to_csv(my_path + "/my_data")
>>> df2 = DataFrame.read_csv(my_path + "/my_data" + "_LOCALE0000")
>>> display(df2)

	A	B
0	1	3
1	2	4

to_hdf(path, index=False, columns=None, file_type='distribute')[source]¶

Save DataFrame to disk as hdf5, preserving column names.

Parameters:

path (str) – File path to save data.
index (bool, default=False) – If True, save the index column. By default, do not save the index.
columns (List, default = None) – List of columns to include in the file. If None, writes out all columns.
file_type (str (single | distribute), default=distribute) – Whether to save to a single file or distribute across Locales.

Return type:

None

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray.

Notes

This method saves one file per locale of the arkouda server. All files are prefixed by the path argument and suffixed by their locale number.

See also

to_parquet, load

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import os.path
>>> from pathlib import Path
>>> my_path = os.path.join(os.getcwd(), 'hdf_output')
>>> Path(my_path).mkdir(parents=True, exist_ok=True)

>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]})
>>> df.to_hdf(my_path + "/my_data")
>>> df.load(my_path + "/my_data")

	A	B
0	1	3
1	2	4

to_markdown(mode='wt', index=True, tablefmt='grid', storage_options=None, **kwargs)[source]¶

Print DataFrame in Markdown-friendly format.

Parameters:

mode (str, optional) – Mode in which file is opened, “wt” by default.
index (bool, optional, default True) – Add index (row) labels.
tablefmt (str = "grid") – Table format to call from tablulate: https://pypi.org/project/tabulate/
storage_options (dict, optional) – Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc., if using a URL that will be parsed by fsspec, e.g., starting “s3://”, “gcs://”. An error will be raised if providing this argument with a non-fsspec URL. See the fsspec and backend storage implementation docs for the set of allowed keys and values.
**kwargs – These parameters will be passed to tabulate.

Note

This function should only be called on small DataFrames as it calls pandas.DataFrame.to_markdown: https://pandas.pydata.org/pandas-docs/version/1.2.4/reference/api/pandas.DataFrame.to_markdown.html

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]})
>>> print(df.to_markdown())
+----+------------+------------+
|    | animal_1   | animal_2   |
+====+============+============+
|  0 | elk        | dog        |
+----+------------+------------+
|  1 | pig        | quetzal    |
+----+------------+------------+

Suppress the index:

>>> print(df.to_markdown(index = False))
+------------+------------+
| animal_1   | animal_2   |
+============+============+
| elk        | dog        |
+------------+------------+
| pig        | quetzal    |
+------------+------------+

to_pandas(datalimit=1073741824, retain_index=False)[source]¶

Send this DataFrame to a pandas DataFrame.

Parameters:

datalimit (int, default=arkouda.client.maxTransferBytes) – The maximum number size, in megabytes to transfer. The requested DataFrame will be converted to a pandas DataFrame only if the estimated size of the DataFrame does not exceed this value.
retain_index (bool, default=False) – Normally, to_pandas() creates a new range index object. If you want to keep the index column, set this to True.

Returns:

The result of converting this DataFrame to a pandas DataFrame.

Return type:

pandas.DataFrame

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> ak_df = ak.DataFrame({"A": ak.arange(2), "B": -1 * ak.arange(2)})
>>> type(ak_df)
arkouda.dataframe.DataFrame
>>> display(ak_df)

	A	B
0	0	0
1	1	-1

>>> import pandas as pd
>>> pd_df = ak_df.to_pandas()
>>> type(pd_df)
pandas.core.frame.DataFrame
>>> display(pd_df)

	A	B
0	0	0
1	1	-1

to_parquet(path, index=False, columns=None, compression: str | None = None, convert_categoricals: bool = False)[source]¶

Save DataFrame to disk as parquet, preserving column names.

Parameters:

path (str) – File path to save data.
index (bool, default=False) – If True, save the index column. By default, do not save the index.
columns (list) – List of columns to include in the file. If None, writes out all columns.
compression (str (Optional), default=None) – Provide the compression type to use when writing the file. Supported values: snappy, gzip, brotli, zstd, lz4
convert_categoricals (bool, default=False) – Parquet requires all columns to be the same size and Categoricals don’t satisfy that requirement. If set, write the equivalent Strings in place of any Categorical columns.

Return type:

None

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

This method saves one file per locale of the arkouda server. All files are prefixed by the path argument and suffixed by their locale number.

See also

to_hdf, load

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import os.path
>>> from pathlib import Path
>>> my_path = os.path.join(os.getcwd(), 'parquet_output')
>>> Path(my_path).mkdir(parents=True, exist_ok=True)

>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]})
>>> df.to_parquet(my_path + "/my_data")
>>> df.load(my_path + "/my_data")

	B	A
0	3	1
1	4	2

transfer(hostname, port)[source]¶

Sends a DataFrame to a different Arkouda server.

Parameters:

hostname (str) – The hostname where the Arkouda server intended to receive the DataFrame is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().

Returns:

A message indicating a complete transfer.

Return type:

str

Raises:

ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype

unregister()[source]¶

Unregister this DataFrame object in the arkouda server which was previously registered using register() and/or attached to using attach().

Raises:: RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister.

Notes

Objects registered with the server are immune to deletion until they are unregistered.

Example

>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
>>> df.register("my_table_name")
>>> df.attach("my_table_name")
>>> df.is_registered()
True
>>> df.unregister()
>>> df.is_registered()
False

unregister_dataframe_by_name(user_defined_name: str) → str[source]¶

Function to unregister DataFrame object by name which was registered with the arkouda server via register().

Parameters:

user_defined_name (str) – Name under which the DataFrame object was registered.

Raises:

TypeError – If user_defined_name is not a string.
RegistrationError – If there is an issue attempting to unregister any underlying components.

Example

>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
>>> df.register("my_table_name")
>>> df.attach("my_table_name")
>>> df.is_registered()
True
>>> df.unregister_dataframe_by_name("my_table_name")
>>> df.is_registered()
False

update_hdf(prefix_path: str, index=False, columns=None, repack: bool = True)[source]¶

Overwrite the dataset with the name provided with this dataframe. If the dataset does not exist it is added.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share.
index (bool, default=False) – If True, save the index column. By default, do not save the index.
columns (List, default=None) – List of columns to include in the file. If None, writes out all columns.
repack (bool, default=True) – HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.

Returns:

Success message if successful.

Return type:

str

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray.

Notes

If file does not contain File_Format attribute to indicate how it was saved,: the file name is checked for _LOCALE#### to determine if it is distributed.

If the dataset provided does not exist, it will be added.

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import os.path
>>> from pathlib import Path
>>> my_path = os.path.join(os.getcwd(), 'hdf_output')
>>> Path(my_path).mkdir(parents=True, exist_ok=True)

>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]})
>>> df.to_hdf(my_path + "/my_data")
>>> df.load(my_path + "/my_data")

	A	B
0	1	3
1	2	4

>>> df2 = ak.DataFrame({"A":[5,6],"B":[7,8]})
>>> df2.update_hdf(my_path + "/my_data")
>>> df.load(my_path + "/my_data")

	A	B
0	5	7
1	6	8

update_nrows()[source]¶: Computes the number of rows on the arkouda server and updates the size parameter.

class arkouda.DataFrame(dict=None, /, **kwargs)[source]¶

Bases: collections.UserDict

A DataFrame structure based on arkouda arrays.

Parameters:

initialdata (List or dictionary of lists, tuples, or pdarrays) – Each list/dictionary entry corresponds to one column of the data and should be a homogenous type. Different columns may have different types. If using a dictionary, keys should be strings.
index (Index, pdarray, or Strings) – Index for the resulting frame. Defaults to an integer range.
columns (List, tuple, pdarray, or Strings) – Column labels to use if the data does not include them. Elements must be strings. Defaults to an stringified integer range.

Examples

Create an empty DataFrame and add a column of data:

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame()
>>> df['a'] = ak.array([1,2,3])
>>> display(df)

	a
0	1
1	2
2	3

Create a new DataFrame using a dictionary of data:

>>> userName = ak.array(['Alice', 'Bob', 'Alice', 'Carol', 'Bob', 'Alice'])
>>> userID = ak.array([111, 222, 111, 333, 222, 111])
>>> item = ak.array([0, 0, 1, 1, 2, 0])
>>> day = ak.array([5, 5, 6, 5, 6, 6])
>>> amount = ak.array([0.5, 0.6, 1.1, 1.2, 4.3, 0.6])
>>> df = ak.DataFrame({'userName': userName, 'userID': userID,
>>>            'item': item, 'day': day, 'amount': amount})
>>> display(df)

	userName	userID	item	day	amount
0	Alice	111	0	5	0.5
1	Bob	222	0	5	0.6
2	Alice	111	1	6	1.1
3	Carol	333	1	5	1.2
4	Bob	222	2	6	4.3
5	Alice	111	0	6	0.6

Indexing works slightly differently than with pandas:

>>> df[0]

keys	values
userName	Alice
userID	111
item	0
day	5
amount	0.5

>>> df['userID']
array([111, 222, 111, 333, 222, 111])

>>> df['userName']
array(['Alice', 'Bob', 'Alice', 'Carol', 'Bob', 'Alice'])

>>> df[ak.array([1,3,5])]

	userName	userID	item	day	amount
0	Bob	222	0	5	0.6
1	Carol	333	1	5	1.2
2	Alice	111	0	6	0.6

Compute the stride:

>>> df[1:5:1]

	userName	userID	item	day	amount
0	Bob	222	0	5	0.6
1	Alice	111	1	6	1.1
2	Carol	333	1	5	1.2
3	Bob	222	2	6	4.3

>>> df[ak.array([1,2,3])]

	userName	userID	item	day	amount
0	Bob	222	0	5	0.6
1	Alice	111	1	6	1.1
2	Carol	333	1	5	1.2

>>> df[['userID', 'day']]

	userID	day
0	111	5
1	222	5
2	111	6
3	333	5
4	222	6
5	111	6

GroupBy(keys, use_series=False, as_index=True, dropna=True)[source]¶

Group the dataframe by a column or a list of columns.

Parameters:

keys (str or list of str) – An (ordered) list of column names or a single string to group by.
use_series (bool, default=False) – If True, returns an arkouda.dataframe.DataFrameGroupBy object. Otherwise an arkouda.groupbyclass.GroupBy object.
as_index (bool, default=True) – If True, groupby columns will be set as index otherwise, the groupby columns will be treated as DataFrame columns.
dropna (bool, default=True) – If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.

Returns:

If use_series = True, returns an arkouda.dataframe.DataFrameGroupBy object. Otherwise returns an arkouda.groupbyclass.GroupBy object.

Return type:

See also

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1.0, 1.0, 2.0, np.nan], 'col2': [4, 5, 6, 7]})
>>> df

	col1	col2
0	1	4
1	1	5
2	2	6
3	nan	7

>>> df.GroupBy("col1")
<arkouda.groupbyclass.GroupBy at 0x7f2cf23e10c0>
>>> df.GroupBy("col1").size()
(array([1.00000000000000000 2.00000000000000000]), array([2 1]))

>>> df.GroupBy("col1",use_series=True)
col1
1.0    2
2.0    1
dtype: int64
>>> df.GroupBy("col1",use_series=True, as_index = False).size()

	col1	size
0	1	2
1	2	1

all(axis=0) → Series | bool[source]¶

Return whether all elements are True, potentially over an axis.

Returns True unless there at least one element along a Dataframe axis that is False.

Currently, will ignore any columns that are not type bool. This is equivalent to the pandas option bool_only=True.

Parameters:

axis ({0 or ‘index’, 1 or ‘columns’, None}, default = 0) –

Indicate which axis or axes should be reduced.

0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.

1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.

None : reduce all axes, return a scalar.

Return type:

Raises:

ValueError – Raised if axis does not have a value in {0 or ‘index’, 1 or ‘columns’, None}.

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({"A":[True,True,True,False],"B":[True,True,True,False],
...          "C":[True,False,True,False],"D":[True,True,True,True]})

	A	B	C	D
0	True	True	True	True
1	True	True	False	True
2	True	True	True	True
3	False	False	False	True

>>> df.all(axis=0)
A    False
B    False
C    False
D     True
dtype: bool
>>> df.all(axis=1)
0     True
1    False
2     True
3    False
dtype: bool
>>> df.all(axis=None)
False

any(axis=0) → Series | bool[source]¶

Return whether any element is True, potentially over an axis.

Returns False unless there is at least one element along a Dataframe axis that is True.

Currently, will ignore any columns that are not type bool. This is equivalent to the pandas option bool_only=True.

Parameters:

axis ({0 or ‘index’, 1 or ‘columns’, None}, default = 0) –

Indicate which axis or axes should be reduced.

0 / ‘index’ : reduce the index, return a Series whose index is the original column labels.

1 / ‘columns’ : reduce the columns, return a Series whose index is the original index.

None : reduce all axes, return a scalar.

Return type:

Raises:

ValueError – Raised if axis does not have a value in {0 or ‘index’, 1 or ‘columns’, None}.

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({"A":[True,True,True,False],"B":[True,True,True,False],
...          "C":[True,False,True,False],"D":[False,False,False,False]})

	A	B	C	D
0	True	True	True	False
1	True	True	False	False
2	True	True	True	False
3	False	False	False	False

>>> df.any(axis=0)
A     True
B     True
C     True
D    False
dtype: bool
>>> df.any(axis=1)
0     True
1     True
2     True
3    False
dtype: bool
>>> df.any(axis=None)
True

append(other, ordered=True)[source]¶

Concatenate data from ‘other’ onto the end of this DataFrame, in place.

Parameters:

other (DataFrame) – The DataFrame object whose data will be appended to this DataFrame.
ordered (bool, default=True) – If False, allow rows to be interleaved for better performance (but data within a row remains together). By default, append all rows to the end, in input order.

Returns:

Appending occurs in-place, but result is returned for compatibility.

Return type:

self

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df1 = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]})

	col1	col2
0	1	3
1	2	4

>>> df2 = ak.DataFrame({'col1': [3], 'col2': [5]})

	col1	col2
0	3	5

>>> df1.append(df2)
>>> df1

	col1	col2
0	1	3
1	2	4
2	3	5

apply_permutation(perm)[source]¶

Apply a permutation to an entire DataFrame. The operation is done in place and the original DataFrame will be modified.

This may be useful if you want to unsort an DataFrame, or even to apply an arbitrary permutation such as the inverse of a sorting permutation.

Parameters:: perm (pdarray) – A permutation array. Should be the same size as the data arrays, and should consist of the integers [0,size-1] in some order. Very minimal testing is done to ensure this is a permutation.
Return type:: None

See also

sort

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})

	col1	col2
0	1	4
1	2	5
2	3	6

>>> perm_arry = ak.array([0, 2, 1])
>>> df.apply_permutation(perm_arry)
>>> display(df)

	col1	col2
0	1	4
1	3	6
2	2	5

argsort(key, ascending=True)[source]¶

Return the permutation that sorts the dataframe by key.

Parameters:

key (str) – The key to sort on.
ascending (bool, default = True) – If true, sort the key in ascending order. Otherwise, sort the key in descending order.

Returns:

The permutation array that sorts the data on key.

Return type:

See also

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1.1, 3.1, 2.1], 'col2': [6, 5, 4]})
>>> display(df)

	col1	col2
0	1.1	6
1	3.1	5
2	2.1	4

>>> df.argsort('col1')
array([0 2 1])
>>> sorted_df1 = df[df.argsort('col1')]
>>> display(sorted_df1)

	col1	col2
0	1.1	6
1	2.1	4
2	3.1	5

>>> df.argsort('col2')
array([2 1 0])
>>> sorted_df2 = df[df.argsort('col2')]
>>> display(sorted_df2)

	col1	col2
0	2.1	4
1	3.1	5
2	1.1	6

assign(**kwargs) → DataFrame[source]¶

Assign new columns to a DataFrame.

Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.

Parameters:: **kwargs (dict of {str: callable or Series}) – The column names are keywords. If the values are callable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn’t check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.
Returns:: A new DataFrame with the new columns in addition to all the existing columns.
Return type:: DataFrame

Notes

Examples

>>> df = ak.DataFrame({'temp_c': [17.0, 25.0]},
...                   index=['Portland', 'Berkeley'])
>>> df
          temp_c
Portland    17.0
Berkeley    25.0

Where the value is a callable, evaluated on df:

>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

Alternatively, the same behavior can be achieved by directly referencing an existing Series or sequence:

>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
          temp_c  temp_f
Portland    17.0    62.6
Berkeley    25.0    77.0

You can create multiple columns within the same assign where one of the columns depends on another one defined within the same assign:

>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
...           temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9)
          temp_c  temp_f  temp_k
Portland    17.0    62.6  290.15
Berkeley    25.0    77.0  298.15

attach(user_defined_name: str) → DataFrame[source]¶

Function to return a DataFrame object attached to the registered name in the arkouda server which was registered using register().

Parameters:: user_defined_name (str) – user defined name which DataFrame object was registered under.
Returns:: The DataFrame object created by re-attaching to the corresponding server components.
Return type:: arkouda.dataframe.DataFrame
Raises:: RegistrationError – if user_defined_name is not registered

Example

>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
>>> df.register("my_table_name")
>>> df.attach("my_table_name")
>>> df.is_registered()
True
>>> df.unregister()
>>> df.is_registered()
False

coargsort(keys, ascending=True)[source]¶

Return the permutation that sorts the dataframe by keys.

Note: Sorting using Strings may not yield correct sort order.

Parameters:: keys (list of str) – The keys to sort on.
Returns:: The permutation array that sorts the data on keys.
Return type:: arkouda.numpy.pdarrayclass.pdarray

Example

>>> df = ak.DataFrame({'col1': [2, 2, 1], 'col2': [3, 4, 3], 'col3':[5, 6, 7]})
>>> display(df)

	col1	col2	col3
0	2	3	5
1	2	4	6
2	1	3	7

>>> df.coargsort(['col1', 'col2'])
array([2 0 1])
>>>

property columns¶

An Index where the values are the column names of the dataframe.

Returns:: The values of the index are the column names of the dataframe.
Return type:: arkouda.index.Index

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df

	col1	col2
0	1	3
1	2	4

>>> df.columns
Index(array(['col1', 'col2']), dtype='<U0')

concat(items, ordered=True)[source]¶: Essentially an append, but different formatting.

corr() → DataFrame[source]¶

Return new DataFrame with pairwise correlation of columns.

Returns:: Arkouda DataFrame containing correlation matrix of all columns.
Return type:: arkouda.dataframe.DataFrame
Raises:: RuntimeError – Raised if there’s a server-side error thrown.

See also

pdarray.corr

Notes

Generates the correlation matrix using Pearson R for all columns.

Attempts to convert to numeric values where possible for inclusion in the matrix.

Example

>>> df = ak.DataFrame({'col1': [1, 2], 'col2': [-1, -2]})
>>> display(df)

	col1	col2
0	1	-1
1	2	-2

>>> corr = df.corr()

	col1	col2
col1	1	-1
col2	-1	1

count(axis: int | str = 0, numeric_only=False) → Series[source]¶

Count non-NA cells for each column or row.

The values np.NaN are considered NA.

Parameters:

axis ({0 or 'index', 1 or 'columns'}, default 0) – If 0 or ‘index’ counts are generated for each column. If 1 or ‘columns’ counts are generated for each row.
numeric_only (bool = False) – Include only float, int or boolean data.

Returns:

For each column/row the number of non-NA/null entries.

Return type:

arkouda.series.Series

Raises:

ValueError – Raised if axis is not 0, 1, ‘index’, or ‘columns’.

See also

GroupBy.count

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import numpy as np
>>> df = ak.DataFrame({'col_A': ak.array([7, np.nan]), 'col_B':ak.array([1, 9])})
>>> display(df)

	col_A	col_B
0	7	1
1	nan	9

>>> df.count()
col_A    1
col_B    2
dtype: int64

>>> df = ak.DataFrame({'col_A': ak.array(["a","b","c"]), 'col_B':ak.array([1, np.nan, np.nan])})
>>> display(df)

	col_A	col_B
0	a	1
1	b	nan
2	c	nan

>>> df.count()
col_A    3
col_B    1
dtype: int64

>>> df.count(numeric_only=True)
col_B    1
dtype: int64

>>> df.count(axis=1)
0    2
1    1
2    1
dtype: int64

Drop column/s or row/s from the dataframe.

Parameters:

keys (str, int or list) – The labels to be dropped on the given axis.
axis (int or str) – The axis on which to drop from. 0/’index’ - drop rows, 1/’columns’ - drop columns.
inplace (bool, default=False) – When True, perform the operation on the calling object. When False, return a new object.

Returns:

DateFrame when inplace=False; None when inplace=True

Return type:

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> display(df)

	col1	col2
0	1	3
1	2	4

Drop column

>>> df.drop('col1', axis = 1)

	col2
0	3
1	4

Drop row

>>> df.drop(0, axis = 0)

	col1	col2
0	2	4

drop_duplicates(subset=None, keep='first')[source]¶

Drops duplcated rows and returns resulting DataFrame.

If a subset of the columns are provided then only one instance of each duplicated row will be returned (keep determines which row).

Parameters:

subset (Iterable) – Iterable of column names to use to dedupe.
keep ({'first', 'last'}, default='first') – Determines which duplicates (if any) to keep.

Returns:

DataFrame with duplicates removed.

Return type:

Example

>>> df = ak.DataFrame({'col1': [1, 2, 2, 3], 'col2': [4, 5, 5, 6]})
>>> display(df)

	col1	col2
0	1	4
1	2	5
2	2	5
3	3	6

>>> df.drop_duplicates()

	col1	col2
0	1	4
1	2	5
2	3	6

dropna(axis: int | str = 0, how: str | None = None, thresh: int | None = None, ignore_index: bool = False) → DataFrame[source]¶

Remove missing values.

Parameters:

axis ({0 or 'index', 1 or 'columns'}, default = 0) –
Determine if rows or columns which contain missing values are removed.

0, or ‘index’: Drop rows which contain missing values.

1, or ‘columns’: Drop columns which contain missing value.

Only a single axis is allowed.
how ({'any', 'all'}, default='any') –
Determine if row or column is removed from DataFrame, when we have at least one NA or all NA.

’any’: If any NA values are present, drop that row or column.

’all’: If all values are NA, drop that row or column.
thresh (int, optional) – Require that many non - NA values.Cannot be combined with how.
ignore_index (bool, default False) – If True, the resulting axis will be labeled 0, 1, …, n - 1.

Returns:

DataFrame with NA entries dropped from it.

Return type:

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import numpy as np
>>> df = ak.DataFrame(
    {
        "A": [True, True, True, True],
        "B": [1, np.nan, 2, np.nan],
        "C": [1, 2, 3, np.nan],
        "D": [False, False, False, False],
        "E": [1, 2, 3, 4],
        "F": ["a", "b", "c", "d"],
        "G": [1, 2, 3, 4],
    }
   )

>>> display(df)

	A	B	C	D	E	F	G
0	True	1	1	False	1	a	1
1	True	nan	2	False	2	b	2
2	True	2	3	False	3	c	3
3	True	nan	nan	False	4	d	4

>>> df.dropna()

	A	B	C	D	E	F	G
0	True	1	1	False	1	a	1
1	True	2	3	False	3	c	3

>>> df.dropna(axis=1)

	A	D	E	F	G
0	True	False	1	a	1
1	True	False	2	b	2
2	True	False	3	c	3
3	True	False	4	d	4

>>> df.dropna(axis=1, thresh=3)

	A	C	D	E	F	G
0	True	1	False	1	a	1
1	True	2	False	2	b	2
2	True	3	False	3	c	3
3	True	nan	False	4	d	4

>>> df.dropna(axis=1, how="all")

	A	B	C	D	E	F	G
0	True	1	1	False	1	a	1
1	True	nan	2	False	2	b	2
2	True	2	3	False	3	c	3
3	True	nan	nan	False	4	d	4

property dtypes: DataFrame¶

The dtypes of the dataframe.

Returns:: dtypes – The dtypes of the dataframe.
Return type:: arkouda.row.Row

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1, 2], 'col2': ["a", "b"]})
>>> df

	col1	col2
0	1	a
1	2	b

>>> df.dtypes

keys	values
col1	int64
col2	str

property empty: DataFrame¶

Whether the dataframe is empty.

Returns:: True if the dataframe is empty, otherwise False.
Return type:: bool

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({})
>>> df
 0 rows x 0 columns
>>> df.empty
True

filter_by_range(keys, low=1, high=None)[source]¶

Find all rows where the value count of the items in a given set of columns (keys) is within the range [low, high].

To filter by a specific value, set low == high.

Parameters:

keys (str or list of str) – The names of the columns to group by.
low (int, default=1) – The lowest value count.
high (int, default=None) – The highest value count, default to unlimited.

Returns:

An array of boolean values for qualified rows in this DataFrame.

Return type:

arkouda.dataframe.DataFrameGroupBy or arkouda.groupbyclass.GroupBy

Example

>>> df = ak.DataFrame({'col1': [1, 2, 2, 2, 3, 3], 'col2': [4, 5, 6, 7, 8, 9]})
>>> display(df)

	col1	col2
0	1	4
1	2	5
2	2	6
3	2	7
4	3	8
5	3	9

>>> df.filter_by_range("col1", low=1, high=2)
array([True False False False True True])

>>> filtered_df = df[df.filter_by_range("col1", low=1, high=2)]
>>> display(filtered_df)

	col1	col2
0	1	4
1	3	8
2	3	9

from_pandas(pd_df)[source]¶

Copy the data from a pandas DataFrame into a new arkouda.dataframe.DataFrame.

Parameters:: pd_df (pandas.DataFrame) – A pandas DataFrame to convert.
Return type:: arkouda.dataframe.DataFrame

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import pandas as pd
>>> pd_df = pd.DataFrame({"A":[1,2],"B":[3,4]})
>>> type(pd_df)
pandas.core.frame.DataFrame
>>> display(pd_df)

	A	B
0	1	3
1	2	4

>>> ak_df = DataFrame.from_pandas(pd_df)
>>> type(ak_df)
arkouda.dataframe.DataFrame
>>> display(ak_df)

	A	B
0	1	3
1	2	4

from_return_msg(rep_msg)[source]¶

Creates a DataFrame object from an arkouda server response message.

Parameters:: rep_msg (string) – Server response message used to create a DataFrame.
Return type:: arkouda.dataframe.DataFrame

groupby(keys, use_series=True, as_index=True, dropna=True)[source]¶

Group the dataframe by a column or a list of columns. Alias for GroupBy.

Parameters:

keys (str or list of str) – An (ordered) list of column names or a single string to group by.
use_series (bool, default=True) – If True, returns an arkouda.dataframe.DataFrameGroupBy object. Otherwise an arkouda.groupbyclass.GroupBy object.
as_index (bool, default=True) – If True, groupby columns will be set as index otherwise, the groupby columns will be treated as DataFrame columns.
dropna (bool, default=True) – If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.

Returns:

If use_series = True, returns an arkouda.dataframe.DataFrameGroupBy object. Otherwise returns an arkouda.groupbyclass.GroupBy object.

Return type:

See also

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1.0, 1.0, 2.0, np.nan], 'col2': [4, 5, 6, 7]})
>>> df

	col1	col2
0	1	4
1	1	5
2	2	6
3	nan	7

>>> df.GroupBy("col1")
<arkouda.groupbyclass.GroupBy at 0x7f2cf23e10c0>
>>> df.GroupBy("col1").size()
(array([1.00000000000000000 2.00000000000000000]), array([2 1]))

>>> df.GroupBy("col1",use_series=True)
col1
1.0    2
2.0    1
dtype: int64
>>> df.GroupBy("col1",use_series=True, as_index = False).size()

	col1	size
0	1	2
1	2	1

head(n=5)[source]¶

Return the first n rows.

This function returns the first n rows of the the dataframe. It is useful for quickly verifying data, for example, after sorting or appending rows.

Parameters:: n (int, default = 5) – Number of rows to select.
Returns:: The first n rows of the DataFrame.
Return type:: arkouda.dataframe.DataFrame

See also

tail

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': ak.arange(10), 'col2': -1 * ak.arange(10)})
>>> display(df)

	col1	col2
0	0	0
1	1	-1
2	2	-2
3	3	-3
4	4	-4
5	5	-5
6	6	-6
7	7	-7
8	8	-8
9	9	-9

>>> df.head()

	col1	col2
0	0	0
1	1	-1
2	2	-2
3	3	-3
4	4	-4

>>> df.head(n=2)

	col1	col2
0	0	0
1	1	-1

property index¶

The index of the dataframe.

Returns:: The index of the dataframe.
Return type:: arkouda.index.Index or arkouda.index.MultiIndex

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df

	col1	col2
0	1	3
1	2	4

>>> df.index
Index(array([0 1]), dtype='int64')

property info¶

Returns a summary string of this dataframe.

Returns:: A summary string of this dataframe.
Return type:: str

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1, 2], 'col2': ["a", "b"]})
>>> df

	col1	col2
0	1	a
1	2	b

>>> df.info
"DataFrame(['col1', 'col2'], 2 rows, 20 B)"

is_registered() → bool[source]¶

Return True if the object is contained in the registry.

Returns:: Indicates if the object is contained in the registry.
Return type:: bool
Raises:: RegistrationError – Raised if there’s a server-side error or a mismatch of registered components.

Notes

Objects registered with the server are immune to deletion until they are unregistered.

Example

>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
>>> df.register("my_table_name")
>>> df.attach("my_table_name")
>>> df.is_registered()
True
>>> df.unregister()
>>> df.is_registered()
False

isin(values: pdarray | Dict | Series | DataFrame) → DataFrame[source]¶

Determine whether each element in the DataFrame is contained in values.

Parameters:: values (pdarray, dict, Series, or DataFrame) – The values to check for in DataFrame. Series can only have a single index.
Returns:: Arkouda DataFrame of booleans showing whether each element in the DataFrame is contained in values.
Return type:: arkouda.dataframe.DataFrame

See also

ak.Series.isin

Notes

Pandas supports values being an iterable type. In arkouda, we replace this with pdarray.
Pandas supports ~ operations. Currently, ak.DataFrame does not support this.

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col_A': ak.array([7, 3]), 'col_B':ak.array([1, 9])})
>>> display(df)

	col_A	col_B
0	7	1
1	3	9

When values is a pdarray, check every value in the DataFrame to determine if it exists in values.

>>> df.isin(ak.array([0, 1]))

	col_A	col_B
0	0	1
1	0	0

When values is a dict, the values in the dict are passed to check the column indicated by the key.

>>> df.isin({'col_A': ak.array([0, 3])})

	col_A	col_B
0	0	0
1	1	0

When values is a Series, each column is checked if values is present positionally. This means that for True to be returned, the indexes must be the same.

>>> i = ak.Index(ak.arange(2))
>>> s = ak.Series(data=[3, 9], index=i)
>>> df.isin(s)

	col_A	col_B
0	0	0
1	0	1

When values is a DataFrame, the index and column must match. Note that 9 is not found because the column name does not match.

>>> other_df = ak.DataFrame({'col_A':ak.array([7, 3]), 'col_C':ak.array([0, 9])})
>>> df.isin(other_df)

	col_A	col_B
0	1	0
1	1	0

isna() → DataFrame[source]¶

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. numpy.NaN values get mapped to True values. Everything else gets mapped to False values.

Returns:: Mask of bool values for each element in DataFrame that indicates whether an element is an NA value.
Return type:: arkouda.dataframe.DataFrame

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import numpy as np
>>> df = ak.DataFrame({"A": [np.nan, 2, 2, 3], "B": [3, np.nan, 5, 6],
...          "C": [1, np.nan, 2, np.nan], "D":["a","b","c","d"]})
>>> display(df)

	A	B	C	D
0	nan	3	1	a
1	2	nan	nan	b
2	2	5	2	c
3	3	6	nan	d

>>> df.isna()
       A      B      C      D
0   True  False  False  False
1  False   True   True  False
2  False  False  False  False
3  False  False   True  False (4 rows x 4 columns)

load(prefix_path, file_format='INFER')[source]¶

Load dataframe from file. file_format needed for consistency with other load functions.

Parameters:

prefix_path (str) – The prefix path for the data.
file_format (string, default = "INFER")

Returns:

A dataframe loaded from the prefix_path.

Return type:

Examples

To store data in <my_dir>/my_data_LOCALE0000, use “<my_dir>/my_data” as the prefix.

>>> import arkouda as ak
>>> ak.connect()
>>> import os.path
>>> from pathlib import Path
>>> my_path = os.path.join(os.getcwd(), 'hdf5_output','my_data')
>>> Path(my_path).mkdir(parents=True, exist_ok=True)
>>> df = ak.DataFrame({"A": ak.arange(5), "B": -1 * ak.arange(5)})
>>> df.save(my_path, file_type="distribute")
>>> df.load(my_path)

	A	B
0	0	0
1	1	-1
2	2	-2
3	3	-3
4	4	-4

memory_usage(index=True, unit='B') → Series[source]¶

Return the memory usage of each column in bytes.

The memory usage can optionally include the contribution of the index.

Parameters:

index (bool, default True) – Specifies whether to include the memory usage of the DataFrame’s index in returned Series. If index=True, the memory usage of the index is the first item in the output.
unit (str, default = "B") – Unit to return. One of {‘B’, ‘KB’, ‘MB’, ‘GB’}.

Returns:

A Series whose index is the original column names and whose values is the memory usage of each column in bytes.

Return type:

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> dtypes = [ak.int64, ak.float64,  ak.bool]
>>> data = dict([(str(t), ak.ones(5000, dtype=ak.int64).astype(t)) for t in dtypes])
>>> df = ak.DataFrame(data)
>>> display(df.head())

	int64	float64	bool
0	1	1	True
1	1	1	True
2	1	1	True
3	1	1	True
4	1	1	True

>>> df.memory_usage()

	0
Index	40000
int64	40000
float64	40000
bool	5000

>>> df.memory_usage(index=False)

	0
int64	40000
float64	40000
bool	5000

>>> df.memory_usage(unit="KB")

	0
Index	39.0625
int64	39.0625
float64	39.0625
bool	4.88281

To get the approximate total memory usage:

>>>  df.memory_usage(index=True).sum()

memory_usage_info(unit='GB')[source]¶

A formatted string representation of the size of this DataFrame.

Parameters:: unit (str, default = "GB") – Unit to return. One of {‘KB’, ‘MB’, ‘GB’}.
Returns:: A string representation of the number of bytes used by this DataFrame in [unit]s.
Return type:: str

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': ak.arange(1000), 'col2': ak.arange(1000)})
>>> df.memory_usage_info()
'0.00 GB'

>>> df.memory_usage_info(unit="KB")
'15 KB'

Merge Arkouda DataFrames with a database-style join. The resulting dataframe contains rows from both DataFrames as specified by the merge condition (based on the “how” and “on” parameters).

Based on pandas merge functionality. https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html

Parameters:

right (DataFrame) – The Right DataFrame to be joined.
on (Optional[Union[str, List[str]]] = None) – The name or list of names of the DataFrame column(s) to join on. If on is None, this defaults to the intersection of the columns in both DataFrames.
how ({"inner", "left", "right}, default = "inner") – The merge condition. Must be “inner”, “left”, or “right”.
left_suffix (str, default = "_x") – A string indicating the suffix to add to columns from the left dataframe for overlapping column names in both left and right. Defaults to “_x”. Only used when how is “inner”.
right_suffix (str, default = "_y") – A string indicating the suffix to add to columns from the right dataframe for overlapping column names in both left and right. Defaults to “_y”. Only used when how is “inner”.
convert_ints (bool = True) – If True, convert columns with missing int values (due to the join) to float64. This is to match pandas. If False, do not convert the column dtypes. This has no effect when how = “inner”.
sort (bool = True) – If True, DataFrame is returned sorted by “on”. Otherwise, the DataFrame is not sorted.

Returns:

Joined Arkouda DataFrame.

Return type:

Note

Multiple column joins are only supported for integer columns.

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> left_df = ak.DataFrame({'col1': ak.arange(5), 'col2': -1 * ak.arange(5)})
>>> display(left_df)

	col1	col2
0	0	0
1	1	-1
2	2	-2
3	3	-3
4	4	-4

>>> right_df = ak.DataFrame({'col1': 2 * ak.arange(5), 'col2': 2 * ak.arange(5)})
>>> display(right_df)

	col1	col2
0	0	0
1	2	2
2	4	4
3	6	6
4	8	8

>>> left_df.merge(right_df, on = "col1")

	col1	col2_x	col2_y
0	0	0	0
1	2	-2	2
2	4	-4	4

>>> left_df.merge(right_df, on = "col1", how = "left")

	col1	col2_y	col2_x
0	0	0	0
1	1	nan	-1
2	2	2	-2
3	3	nan	-3
4	4	4	-4

>>> left_df.merge(right_df, on = "col1", how = "right")

	col1	col2_x	col2_y
0	0	0	0
1	2	-2	2
2	4	-4	4
3	6	nan	6
4	8	nan	8

>>> left_df.merge(right_df, on = "col1", how = "outer")

	col1	col2_y	col2_x
0	0	0	0
1	1	nan	-1
2	2	2	-2
3	3	nan	-3
4	4	4	-4
5	6	6	nan
6	8	8	nan

notna() → DataFrame[source]¶

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. numpy.NaN values get mapped to False values.

Returns:: Mask of bool values for each element in DataFrame that indicates whether an element is not an NA value.
Return type:: arkouda.dataframe.DataFrame

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import numpy as np
>>> df = ak.DataFrame({"A": [np.nan, 2, 2, 3], "B": [3, np.nan, 5, 6],
...          "C": [1, np.nan, 2, np.nan], "D":["a","b","c","d"]})
>>> display(df)

	A	B	C	D
0	nan	3	1	a
1	2	nan	nan	b
2	2	5	2	c
3	3	6	nan	d

>>> df.notna()
       A      B      C     D
0  False   True   True  True
1   True  False  False  True
2   True   True   True  True
3   True   True  False  True (4 rows x 4 columns)

objType(*args, **kwargs)¶

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

read_csv(filename: str, col_delim: str = ',')[source]¶

Parameters:

filename (str) – Filename to read data from.
col_delim (str, default=",") – The delimiter for columns within the data.

Returns:

Arkouda DataFrame containing the columns from the CSV file.

Return type:

Raises:

ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist.
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server.

See also

to_csv

Notes

CSV format is not currently supported by load/load_all operations.
The column delimiter is expected to be the same for column names and data.
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (”\n”) at this time.
Unlike other file formats, CSV files store Strings as their UTF-8 format instead of storing

bytes as uint(8).

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import os.path
>>> from pathlib import Path
>>> my_path = os.path.join(os.getcwd(), 'csv_output','my_data')
>>> Path(my_path).mkdir(parents=True, exist_ok=True)

>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]})
>>> df.to_csv(my_path)
>>> df2 = DataFrame.read_csv(my_path + "_LOCALE0000")
>>> display(df2)

	A	B
0	1	3
1	2	4

register(user_defined_name: str) → DataFrame[source]¶

Parameters:

user_defined_name (str) – User defined name the DataFrame is to be registered under. This will be the root name for underlying components.

Returns:

Return type:

Raises:

TypeError – Raised if user_defined_name is not a str.
RegistrationError – If the server was unable to register the DataFrame with the user_defined_name.

Notes

Objects registered with the server are immune to deletion until they are unregistered.

Any changes made to a DataFrame object after registering with the server may not be reflected in attached copies.

Example

>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
>>> df.register("my_table_name")
>>> df.attach("my_table_name")
>>> df.is_registered()
True
>>> df.unregister()
>>> df.is_registered()
False

Rename indexes or columns according to a mapping.

Parameters:

mapper (callable or dict-like, Optional) – Function or dictionary mapping existing values to new values. Nonexistent names will not raise an error. Uses the value of axis to determine if renaming column or index
column (callable or dict-like, Optional) – Function or dictionary mapping existing column names to new column names. Nonexistent names will not raise an error. When this is set, axis is ignored.
index (callable or dict-like, Optional) – Function or dictionary mapping existing index names to new index names. Nonexistent names will not raise an error. When this is set, axis is ignored.
axis (int or str, default=0) – Indicates which axis to perform the rename. 0/”index” - Indexes 1/”column” - Columns
inplace (bool, default=False) – When True, perform the operation on the calling object. When False, return a new object.

Returns:

DateFrame when inplace=False; None when inplace=True.

Return type:

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({"A": ak.array([1, 2, 3]), "B": ak.array([4, 5, 6])})
>>> display(df)

	A	B
0	1	4
1	2	5
2	3	6

Rename columns using a mapping:

>>> df.rename(column={'A':'a', 'B':'c'})

	a	c
0	1	4
1	2	5
2	3	6

Rename indexes using a mapping:

>>> df.rename(index={0:99, 2:11})

	A	B
0	1	4
1	2	5
2	3	6

Rename using an axis style parameter:

>>> df.rename(str.lower, axis='column')

	a	b
0	1	4
1	2	5
2	3	6

reset_index(size: int | None = None, inplace: bool = False) → None | DataFrame[source]¶

Set the index to an integer range.

Useful if this dataframe is the result of a slice operation from another dataframe, or if you have permuted the rows and no longer need to keep that ordering on the rows.

Parameters:

size (int, optional) – If size is passed, do not attempt to determine size based on existing column sizes. Assume caller handles consistency correctly.
inplace (bool, default=False) – When True, perform the operation on the calling object. When False, return a new object.

Returns:

DateFrame when inplace=False; None when inplace=True.

Return type:

Note

Pandas adds a column ‘index’ to indicate the original index. Arkouda does not currently support this behavior.

Example

>>> df = ak.DataFrame({"A": ak.array([1, 2, 3]), "B": ak.array([4, 5, 6])})
>>> display(df)

	A	B
0	1	4
1	2	5
2	3	6

>>> perm_df = df[ak.array([0,2,1])]
>>> display(perm_df)

	A	B
0	1	4
1	3	6
2	2	5

>>> perm_df.reset_index()

	A	B
0	1	4
1	3	6
2	2	5

sample(n=5)[source]¶

Return a random sample of n rows.

Parameters:: n (int, default=5) – Number of rows to return.
Returns:: The sampled n rows of the DataFrame.
Return type:: arkouda.dataframe.DataFrame

Example

>>> df = ak.DataFrame({"A": ak.arange(5), "B": -1 * ak.arange(5)})
>>> display(df)

	A	B
0	0	0
1	1	-1
2	2	-2
3	3	-3
4	4	-4

Random output of size 3:

>>> df.sample(n=3)

	A	B
0	0	0
1	1	-1
2	4	-4

save(path, index=False, columns=None, file_format='HDF5', file_type='distribute', compression: str | None = None)[source]¶

DEPRECATED Save DataFrame to disk, preserving column names.

Parameters:

path (str) – File path to save data.
index (bool, default=False) – If True, save the index column. By default, do not save the index.
columns (list, default=None) – List of columns to include in the file. If None, writes out all columns.
file_format (str, default='HDF5') – ‘HDF5’ or ‘Parquet’. Defaults to ‘HDF5’
file_type (str, default=distribute) – “single” or “distribute” If single, will right a single file to locale 0.
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Compression type. Only used for Parquet

Notes

This method saves one file per locale of the arkouda server. All files are prefixed by the path argument and suffixed by their locale number.

See also

to_parquet, to_hdf

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import os.path
>>> from pathlib import Path
>>> my_path = os.path.join(os.getcwd(), 'hdf5_output')
>>> Path(my_path).mkdir(parents=True, exist_ok=True)

>>> df = ak.DataFrame({"A": ak.arange(5), "B": -1 * ak.arange(5)})
>>> df.save(my_path + '/my_data', file_type="single")
>>> df.load(my_path + '/my_data')

	A	B
0	0	0
1	1	-1
2	2	-2
3	3	-3
4	4	-4

property shape¶

The shape of the dataframe.

Returns:: Tuple of array dimensions.
Return type:: tuple of int

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
>>> df

	col1	col2
0	1	4
1	2	5
2	3	6

>>> df.shape
(3, 2)

property size¶

Returns the number of bytes on the arkouda server.

Returns:: The number of bytes on the arkouda server.
Return type:: int

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
>>> df

	col1	col2
0	1	4
1	2	5
2	3	6

>>> df.size
6

sort_index(ascending=True)[source]¶

Sort the DataFrame by indexed columns.

Note: Fails on sort order of arkouda.numpy.strings.Strings columns when: multiple columns being sorted.

Parameters:: ascending (bool, default = True) – Sort values in ascending (default) or descending order.

Example

>>> df = ak.DataFrame({'col1': [1.1, 3.1, 2.1], 'col2': [6, 5, 4]},
...          index = Index(ak.array([2,0,1]), name="idx"))

>>> display(df)

idx	col1	col2
0	1.1	6
1	3.1	5
2	2.1	4

>>> df.sort_index()

idx	col1	col2
0	3.1	5
1	2.1	4
2	1.1	6

sort_values(by=None, ascending=True)[source]¶

Sort the DataFrame by one or more columns.

If no column is specified, all columns are used.

Note: Fails on order of arkouda.numpy.strings.Strings columns when multiple columns being sorted.

Parameters:

by (str or list/tuple of str, default = None) – The name(s) of the column(s) to sort by.
ascending (bool, default = True) – Sort values in ascending (default) or descending order.

See also

apply_permutation

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': [2, 2, 1], 'col2': [3, 4, 3], 'col3':[5, 6, 7]})
>>> display(df)

	col1	col2	col3
0	2	3	5
1	2	4	6
2	1	3	7

>>> df.sort_values()

	col1	col2	col3
0	1	3	7
1	2	3	5
2	2	4	6

>>> df.sort_values("col3")

	col1	col2	col3
0	1	3	7
1	2	3	5
2	2	4	6

tail(n=5)[source]¶

Return the last n rows.

This function returns the last n rows for the dataframe. It is useful for quickly testing if your object has the right type of data in it.

Parameters:: n (int, default=5) – Number of rows to select.
Returns:: The last n rows of the DataFrame.
Return type:: arkouda.dataframe.DataFrame

See also

arkouda.dataframe.head

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({'col1': ak.arange(10), 'col2': -1 * ak.arange(10)})
>>> display(df)

	col1	col2
0	0	0
1	1	-1
2	2	-2
3	3	-3
4	4	-4
5	5	-5
6	6	-6
7	7	-7
8	8	-8
9	9	-9

>>> df.tail()

	col1	col2
0	5	-5
1	6	-6
2	7	-7
3	8	-8
4	9	-9

>>> df.tail(n=2)

	col1	col2
0	8	-8
1	9	-9

to_csv(path: str, index: bool = False, columns: List[str] | None = None, col_delim: str = ',', overwrite: bool = False)[source]¶

Parameters:

path (str) – The filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
index (bool, default=False) – If True, the index of the DataFrame will be written to the file as a column.
columns (list of str (Optional)) – Column names to assign when writing data.
col_delim (str, default=",") – Value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
overwrite (bool, default=False) – If True, any existing files matching your provided prefix_path will be overwritten. If False, an error will be returned if existing files are found.

Return type:

None

Raises:

ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist.
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server.

Notes

CSV format is not currently supported by load/load_all operations.
The column delimiter is expected to be the same for column names and data.
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (”\n”) at this time.

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import os.path
>>> from pathlib import Path
>>> my_path = os.path.join(os.getcwd(), 'csv_output')
>>> Path(my_path).mkdir(parents=True, exist_ok=True)

>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]})
>>> df.to_csv(my_path + "/my_data")
>>> df2 = DataFrame.read_csv(my_path + "/my_data" + "_LOCALE0000")
>>> display(df2)

	A	B
0	1	3
1	2	4

to_hdf(path, index=False, columns=None, file_type='distribute')[source]¶

Save DataFrame to disk as hdf5, preserving column names.

Parameters:

path (str) – File path to save data.
index (bool, default=False) – If True, save the index column. By default, do not save the index.
columns (List, default = None) – List of columns to include in the file. If None, writes out all columns.
file_type (str (single | distribute), default=distribute) – Whether to save to a single file or distribute across Locales.

Return type:

None

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray.

Notes

This method saves one file per locale of the arkouda server. All files are prefixed by the path argument and suffixed by their locale number.

See also

to_parquet, load

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import os.path
>>> from pathlib import Path
>>> my_path = os.path.join(os.getcwd(), 'hdf_output')
>>> Path(my_path).mkdir(parents=True, exist_ok=True)

>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]})
>>> df.to_hdf(my_path + "/my_data")
>>> df.load(my_path + "/my_data")

	A	B
0	1	3
1	2	4

to_markdown(mode='wt', index=True, tablefmt='grid', storage_options=None, **kwargs)[source]¶

Print DataFrame in Markdown-friendly format.

Parameters:

mode (str, optional) – Mode in which file is opened, “wt” by default.
index (bool, optional, default True) – Add index (row) labels.
tablefmt (str = "grid") – Table format to call from tablulate: https://pypi.org/project/tabulate/
storage_options (dict, optional) – Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc., if using a URL that will be parsed by fsspec, e.g., starting “s3://”, “gcs://”. An error will be raised if providing this argument with a non-fsspec URL. See the fsspec and backend storage implementation docs for the set of allowed keys and values.
**kwargs – These parameters will be passed to tabulate.

Note

This function should only be called on small DataFrames as it calls pandas.DataFrame.to_markdown: https://pandas.pydata.org/pandas-docs/version/1.2.4/reference/api/pandas.DataFrame.to_markdown.html

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]})
>>> print(df.to_markdown())
+----+------------+------------+
|    | animal_1   | animal_2   |
+====+============+============+
|  0 | elk        | dog        |
+----+------------+------------+
|  1 | pig        | quetzal    |
+----+------------+------------+

Suppress the index:

>>> print(df.to_markdown(index = False))
+------------+------------+
| animal_1   | animal_2   |
+============+============+
| elk        | dog        |
+------------+------------+
| pig        | quetzal    |
+------------+------------+

to_pandas(datalimit=1073741824, retain_index=False)[source]¶

Send this DataFrame to a pandas DataFrame.

Parameters:

datalimit (int, default=arkouda.client.maxTransferBytes) – The maximum number size, in megabytes to transfer. The requested DataFrame will be converted to a pandas DataFrame only if the estimated size of the DataFrame does not exceed this value.
retain_index (bool, default=False) – Normally, to_pandas() creates a new range index object. If you want to keep the index column, set this to True.

Returns:

The result of converting this DataFrame to a pandas DataFrame.

Return type:

pandas.DataFrame

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> ak_df = ak.DataFrame({"A": ak.arange(2), "B": -1 * ak.arange(2)})
>>> type(ak_df)
arkouda.dataframe.DataFrame
>>> display(ak_df)

	A	B
0	0	0
1	1	-1

>>> import pandas as pd
>>> pd_df = ak_df.to_pandas()
>>> type(pd_df)
pandas.core.frame.DataFrame
>>> display(pd_df)

	A	B
0	0	0
1	1	-1

to_parquet(path, index=False, columns=None, compression: str | None = None, convert_categoricals: bool = False)[source]¶

Save DataFrame to disk as parquet, preserving column names.

Parameters:

path (str) – File path to save data.
index (bool, default=False) – If True, save the index column. By default, do not save the index.
columns (list) – List of columns to include in the file. If None, writes out all columns.
compression (str (Optional), default=None) – Provide the compression type to use when writing the file. Supported values: snappy, gzip, brotli, zstd, lz4
convert_categoricals (bool, default=False) – Parquet requires all columns to be the same size and Categoricals don’t satisfy that requirement. If set, write the equivalent Strings in place of any Categorical columns.

Return type:

None

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

This method saves one file per locale of the arkouda server. All files are prefixed by the path argument and suffixed by their locale number.

See also

to_hdf, load

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import os.path
>>> from pathlib import Path
>>> my_path = os.path.join(os.getcwd(), 'parquet_output')
>>> Path(my_path).mkdir(parents=True, exist_ok=True)

>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]})
>>> df.to_parquet(my_path + "/my_data")
>>> df.load(my_path + "/my_data")

	B	A
0	3	1
1	4	2

transfer(hostname, port)[source]¶

Sends a DataFrame to a different Arkouda server.

Parameters:

hostname (str) – The hostname where the Arkouda server intended to receive the DataFrame is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().

Returns:

A message indicating a complete transfer.

Return type:

str

Raises:

ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype

unregister()[source]¶

Unregister this DataFrame object in the arkouda server which was previously registered using register() and/or attached to using attach().

Raises:: RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister.

Notes

Objects registered with the server are immune to deletion until they are unregistered.

Example

>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
>>> df.register("my_table_name")
>>> df.attach("my_table_name")
>>> df.is_registered()
True
>>> df.unregister()
>>> df.is_registered()
False

unregister_dataframe_by_name(user_defined_name: str) → str[source]¶

Function to unregister DataFrame object by name which was registered with the arkouda server via register().

Parameters:

user_defined_name (str) – Name under which the DataFrame object was registered.

Raises:

TypeError – If user_defined_name is not a string.
RegistrationError – If there is an issue attempting to unregister any underlying components.

Example

>>> df = ak.DataFrame({'col1': [1, 2, 3], 'col2': [4, 5, 6]})
>>> df.register("my_table_name")
>>> df.attach("my_table_name")
>>> df.is_registered()
True
>>> df.unregister_dataframe_by_name("my_table_name")
>>> df.is_registered()
False

update_hdf(prefix_path: str, index=False, columns=None, repack: bool = True)[source]¶

Overwrite the dataset with the name provided with this dataframe. If the dataset does not exist it is added.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share.
index (bool, default=False) – If True, save the index column. By default, do not save the index.
columns (List, default=None) – List of columns to include in the file. If None, writes out all columns.
repack (bool, default=True) – HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.

Returns:

Success message if successful.

Return type:

str

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray.

Notes

If file does not contain File_Format attribute to indicate how it was saved,: the file name is checked for _LOCALE#### to determine if it is distributed.

If the dataset provided does not exist, it will be added.

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> import os.path
>>> from pathlib import Path
>>> my_path = os.path.join(os.getcwd(), 'hdf_output')
>>> Path(my_path).mkdir(parents=True, exist_ok=True)

>>> df = ak.DataFrame({"A":[1,2],"B":[3,4]})
>>> df.to_hdf(my_path + "/my_data")
>>> df.load(my_path + "/my_data")

	A	B
0	1	3
1	2	4

>>> df2 = ak.DataFrame({"A":[5,6],"B":[7,8]})
>>> df2.update_hdf(my_path + "/my_data")
>>> df.load(my_path + "/my_data")

	A	B
0	5	7
1	6	8

update_nrows()[source]¶: Computes the number of rows on the arkouda server and updates the size parameter.

class arkouda.DataFrameGroupBy[source]¶

A DataFrame that has been grouped by a subset of columns.

Parameters:

gb_key_names (str or list(str), default=None) – The column name(s) associated with the aggregated columns.
as_index (bool, default=True) – If True, interpret aggregated column as index (only implemented for single dimensional aggregates). Otherwise, treat aggregated column as a dataframe column.

gb¶

GroupBy object, where the aggregation keys are values of column(s) of a dataframe, usually in preparation for aggregating with respect to the other columns.

Type:: arkouda.groupbyclass.GroupBy

df¶

The dataframe containing the original data.

Type:: arkouda.dataframe.DataFrame

gb_key_names¶

The column name(s) associated with the aggregated columns.

Type:: str or list(str)

as_index¶

If True the grouped values of the aggregation keys will be treated as an index.

Type:: bool, default=True

all(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

any(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

argmax(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

argmin(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

broadcast(x, permute=True)[source]¶

Fill each group’s segment with a constant value.

Parameters:

x (Series or pdarray) – The values to put in each group’s segment.
permute (bool, default=True) – If True (default), permute broadcast values back to the ordering of the original array on which GroupBy was called. If False, the broadcast values are grouped by value.

Returns:

A Series with the Index of the original frame and the values of the broadcast.

Return type:

arkouda.series.Series

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> from arkouda.dataframe import DataFrameGroupBy
>>> df = ak.DataFrame({"A":[1,2,2,3],"B":[3,4,5,6]})

	A	B
0	1	3
1	2	4
2	2	5
3	3	6

>>> gb = df.groupby("A")
>>> x = ak.array([10,11,12])
>>> s = DataFrameGroupBy.broadcast(gb, x)
>>> df["C"] = s.values
>>> display(df)

	A	B	C
0	1	3	10
1	2	4	11
2	2	5	11
3	3	6	12

count(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

diff(colname)[source]¶

Create a difference aggregate for the given column.

For each group, the difference between successive values is calculated. Aggregate operations (mean,min,max,std,var) can be done on the results.

Parameters:: colname (str) – Name of the column to compute the difference on.
Returns:: Object containing the differences, which can be aggregated.
Return type:: DiffAggregate

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({"A":[1,2,2,2,3,3],"B":[3,9,11,27,86,100]})
>>> display(df)

	A	B
0	1	3
1	2	9
2	2	11
3	2	27
4	3	86
5	3	100

>>> gb = df.groupby("A")
>>> gb.diff("B").values
array([nan nan 2.00000000000000000 16.00000000000000000 nan 14.00000000000000000])

first(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

head(n: int = 5, sort_index: bool = True) → DataFrame[source]¶

Return the first n rows from each group.

Parameters:

n (int, optional, default = 5) – Maximum number of rows to return for each group. If the number of rows in a group is less than n, all the values from that group will be returned.
sort_index (bool, default = True) – If true, return the DataFrame with indices sorted.

Return type:

arkouda.dataframe.DataFrame or arkouda.series.Series

Examples

>>> import arkouda as ak
>>> from arkouda import *
>>> df = ak.DataFrame({"a":ak.arange(10) %3 , "b":ak.arange(10)})

	a	b
0	0	0
1	1	1
2	2	2
3	0	3
4	1	4
5	2	5
6	0	6
7	1	7
8	2	8
9	0	9

>>> df.groupby("a").head(2)

	a	b
0	0	0
1	0	3
2	1	1
3	1	4
4	2	2
5	2	5

max(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

mean(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

median(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

min(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

mode(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

nunique(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

prod(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

sample(n=None, frac=None, replace=False, weights=None, random_state=None)[source]¶

Return a random sample from each group. You can either specify the number of elements or the fraction of elements to be sampled. random_state can be used for reproducibility

Parameters:

n (int, optional) – Number of items to return for each group. Cannot be used with frac and must be no larger than the smallest group unless replace is True. Default is one if frac is None.
frac (float, optional) – Fraction of items to return. Cannot be used with n.
replace (bool, default False) – Allow or disallow sampling of the same row more than once.
weights (pdarray, optional) – Default None results in equal probability weighting. If passed a pdarray, then values must have the same length as the underlying DataFrame and will be used as sampling probabilities after normalization within each group. Weights must be non-negative with at least one positive element within each group.
random_state (int or ak.random.Generator, optional) – If int, seed for random number generator. If ak.random.Generator, use as given.

Returns:

A new DataFrame containing items randomly sampled from each group sorted according to the grouped columns.

Return type:

DataFrame

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({"A":[3,1,2,1,2,3],"B":[3,4,5,6,7,8]})
>>> display(df)
+----+-----+-----+
|    |   A |   B |
+====+=====+=====+
|  0 |   3 |   3 |
+----+-----+-----+
|  1 |   1 |   4 |
+----+-----+-----+
|  2 |   2 |   5 |
+----+-----+-----+
|  3 |   1 |   6 |
+----+-----+-----+
|  4 |   2 |   7 |
+----+-----+-----+
|  5 |   3 |   8 |
+----+-----+-----+

>>> df.groupby("A").sample(random_state=6)

	A	B
3	1	6
4	2	7
5	3	8

>>> df.groupby("A").sample(frac=0.5, random_state=3, weights=ak.array([1,1,1,0,0,0]))

	A	B
1	1	4
2	2	5
0	3	3

>>> df.groupby("A").sample(n=3, replace=True, random_state=ak.random.default_rng(7))
+----+-----+-----+
|    |   A |   B |
+====+=====+=====+
|  1 |   1 |   4 |
+----+-----+-----+
|  3 |   1 |   6 |
+----+-----+-----+
|  1 |   1 |   4 |
+----+-----+-----+
|  4 |   2 |   7 |
+----+-----+-----+
|  4 |   2 |   7 |
+----+-----+-----+
|  4 |   2 |   7 |
+----+-----+-----+
|  0 |   3 |   3 |
+----+-----+-----+
|  5 |   3 |   8 |
+----+-----+-----+
|  5 |   3 |   8 |
+----+-----+-----+

size(as_series=None, sort_index=True)[source]¶

Compute the size of each value as the total number of rows, including NaN values.

Parameters:

as_series (bool, default=None) – Indicates whether to return arkouda.dataframe.DataFrame (if as_series = False) or arkouda.series.Series (if as_series = True)
sort_index (bool, default=True) – If True, results will be returned with index values sorted in ascending order.

Return type:

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> df = ak.DataFrame({"A":[1,2,2,3],"B":[3,4,5,6]})
>>> display(df)

	A	B
0	1	3
1	2	4
2	2	5
3	3	6

>>> df.groupby("A").size(as_series = False)

	size
0	1
1	2
2	1

std(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

sum(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

tail(n: int = 5, sort_index: bool = True) → DataFrame[source]¶

Return the last n rows from each group.

Parameters:

n (int, optional, default = 5) – Maximum number of rows to return for each group. If the number of rows in a group is less than n, all the rows from that group will be returned.
sort_index (bool, default = True) – If true, return the DataFrame with indices sorted.

Return type:

Examples

>>> import arkouda as ak
>>> from arkouda import *
>>> df = ak.DataFrame({"a":ak.arange(10) %3 , "b":ak.arange(10)})

	a	b
0	0	0
1	1	1
2	2	2
3	0	3
4	1	4
5	2	5
6	0	6
7	1	7
8	2	8
9	0	9

>>> df.groupby("a").tail(2)

	a	b
0	0	6
1	0	9
2	1	4
3	1	7
4	2	5
5	2	8

unique(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

var(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

xor(colnames=None)¶

Aggregate the operation, with the grouped column(s) values as keys.

Parameters:: colnames ((list of) str, default=None) – Column name or list of column names to compute the aggregation over.
Return type:: arkouda.dataframe.DataFrame

class arkouda.Datetime(pda, unit: str = _BASE_UNIT)[source]¶

Bases: _AbstractBaseTime

Represents a date and/or time.

Datetime is the Arkouda analog to pandas DatetimeIndex and other timeseries data types.

Parameters:

pda (int64 pdarray, pd.DatetimeIndex, pd.Series, or np.datetime64 array)
unit (str, default 'ns') –
For int64 pdarray, denotes the unit of the input. Ignored for pandas and numpy arrays, which carry their own unit. Not case-sensitive; prefixes of full names (like ‘sec’) are accepted.

Possible values:
- ’weeks’ or ‘w’
- ’days’ or ‘d’
- ’hours’ or ‘h’
- ’minutes’, ‘m’, or ‘t’
- ’seconds’ or ‘s’
- ’milliseconds’, ‘ms’, or ‘l’
- ’microseconds’, ‘us’, or ‘u’
- ’nanoseconds’, ‘ns’, or ‘n’
Unlike in pandas, units cannot be combined or mixed with integers

Notes

The .values attribute is always in nanoseconds with int64 dtype.

property date¶

property day¶

property day_of_week¶

property day_of_year¶

property dayofweek¶

property dayofyear¶

property hour¶

property is_leap_year¶

is_registered() → numpy.bool_[source]¶

Return True iff the object is contained in the registry or is a component of a registered object.

Returns:: Indicates if the object is contained in the registry
Return type:: numpy.bool
Raises:: RegistrationError – Raised if there’s a server-side error or a mis-match of registered components

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

isocalendar()[source]¶

property microsecond¶

property millisecond¶

property minute¶

property month¶

property nanosecond¶

register(user_defined_name)[source]¶

Parameters:

user_defined_name (str) – user defined name the Datetime is to be registered under, this will be the root name for underlying components

Returns:

The same Datetime which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different Datetimes with the same name.

Return type:

Datetime

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Datetimes with the user_defined_name

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

property second¶

special_objType = 'Datetime'¶

sum()[source]¶

Return sum of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numpy_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numpy_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.sum(ak.array([1,2,3,4,5]))
15
>>> ak.sum(ak.array([5.5,4.5,3.5,2.5,1.5]))
17.5
>>> ak.array([[1,2,3],[5,4,3]]).sum(axis=1)
array([6 12])

Notes

Works as a method of a pdarray (e.g. a.sum()) or a standalone function (e.g. ak.sum(a))

supported_opeq¶

supported_with_datetime¶

supported_with_pdarray¶

supported_with_r_datetime¶

supported_with_r_pdarray¶

supported_with_r_timedelta¶

supported_with_timedelta¶

to_pandas()[source]¶: Convert array to a pandas DatetimeIndex. Note: if the array size exceeds client.maxTransferBytes, a RuntimeError is raised.

See also

to_ndarray

unregister()[source]¶

Unregister this Datetime object in the arkouda server which was previously registered using register() and/or attached to using attach()

Raises:: RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

property week¶

property weekday¶

property weekofyear¶

property year¶

class arkouda.Datetime(pda, unit: str = _BASE_UNIT)[source]¶

Bases: _AbstractBaseTime

Represents a date and/or time.

Datetime is the Arkouda analog to pandas DatetimeIndex and other timeseries data types.

Parameters:

pda (int64 pdarray, pd.DatetimeIndex, pd.Series, or np.datetime64 array)
unit (str, default 'ns') –
For int64 pdarray, denotes the unit of the input. Ignored for pandas and numpy arrays, which carry their own unit. Not case-sensitive; prefixes of full names (like ‘sec’) are accepted.

Possible values:
- ’weeks’ or ‘w’
- ’days’ or ‘d’
- ’hours’ or ‘h’
- ’minutes’, ‘m’, or ‘t’
- ’seconds’ or ‘s’
- ’milliseconds’, ‘ms’, or ‘l’
- ’microseconds’, ‘us’, or ‘u’
- ’nanoseconds’, ‘ns’, or ‘n’
Unlike in pandas, units cannot be combined or mixed with integers

Notes

The .values attribute is always in nanoseconds with int64 dtype.

property date¶

property day¶

property day_of_week¶

property day_of_year¶

property dayofweek¶

property dayofyear¶

property hour¶

property is_leap_year¶

is_registered() → numpy.bool_[source]¶

Return True iff the object is contained in the registry or is a component of a registered object.

Returns:: Indicates if the object is contained in the registry
Return type:: numpy.bool
Raises:: RegistrationError – Raised if there’s a server-side error or a mis-match of registered components

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

isocalendar()[source]¶

property microsecond¶

property millisecond¶

property minute¶

property month¶

property nanosecond¶

register(user_defined_name)[source]¶

Parameters:

user_defined_name (str) – user defined name the Datetime is to be registered under, this will be the root name for underlying components

Returns:

Return type:

Datetime

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Datetimes with the user_defined_name

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

property second¶

special_objType = 'Datetime'¶

sum()[source]¶

Return sum of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numpy_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numpy_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.sum(ak.array([1,2,3,4,5]))
15
>>> ak.sum(ak.array([5.5,4.5,3.5,2.5,1.5]))
17.5
>>> ak.array([[1,2,3],[5,4,3]]).sum(axis=1)
array([6 12])

Notes

Works as a method of a pdarray (e.g. a.sum()) or a standalone function (e.g. ak.sum(a))

supported_opeq¶

supported_with_datetime¶

supported_with_pdarray¶

supported_with_r_datetime¶

supported_with_r_pdarray¶

supported_with_r_timedelta¶

supported_with_timedelta¶

to_pandas()[source]¶: Convert array to a pandas DatetimeIndex. Note: if the array size exceeds client.maxTransferBytes, a RuntimeError is raised.

See also

to_ndarray

unregister()[source]¶

Unregister this Datetime object in the arkouda server which was previously registered using register() and/or attached to using attach()

Raises:: RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

property week¶

property weekday¶

property weekofyear¶

property year¶

class arkouda.Datetime(pda, unit: str = _BASE_UNIT)[source]¶

Bases: _AbstractBaseTime

Represents a date and/or time.

Datetime is the Arkouda analog to pandas DatetimeIndex and other timeseries data types.

Parameters:

pda (int64 pdarray, pd.DatetimeIndex, pd.Series, or np.datetime64 array)
unit (str, default 'ns') –
For int64 pdarray, denotes the unit of the input. Ignored for pandas and numpy arrays, which carry their own unit. Not case-sensitive; prefixes of full names (like ‘sec’) are accepted.

Possible values:
- ’weeks’ or ‘w’
- ’days’ or ‘d’
- ’hours’ or ‘h’
- ’minutes’, ‘m’, or ‘t’
- ’seconds’ or ‘s’
- ’milliseconds’, ‘ms’, or ‘l’
- ’microseconds’, ‘us’, or ‘u’
- ’nanoseconds’, ‘ns’, or ‘n’
Unlike in pandas, units cannot be combined or mixed with integers

Notes

The .values attribute is always in nanoseconds with int64 dtype.

property date¶

property day¶

property day_of_week¶

property day_of_year¶

property dayofweek¶

property dayofyear¶

property hour¶

property is_leap_year¶

is_registered() → numpy.bool_[source]¶

Return True iff the object is contained in the registry or is a component of a registered object.

Returns:: Indicates if the object is contained in the registry
Return type:: numpy.bool
Raises:: RegistrationError – Raised if there’s a server-side error or a mis-match of registered components

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

isocalendar()[source]¶

property microsecond¶

property millisecond¶

property minute¶

property month¶

property nanosecond¶

register(user_defined_name)[source]¶

Parameters:

user_defined_name (str) – user defined name the Datetime is to be registered under, this will be the root name for underlying components

Returns:

Return type:

Datetime

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Datetimes with the user_defined_name

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

property second¶

special_objType = 'Datetime'¶

sum()[source]¶

Return sum of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numpy_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numpy_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.sum(ak.array([1,2,3,4,5]))
15
>>> ak.sum(ak.array([5.5,4.5,3.5,2.5,1.5]))
17.5
>>> ak.array([[1,2,3],[5,4,3]]).sum(axis=1)
array([6 12])

Notes

Works as a method of a pdarray (e.g. a.sum()) or a standalone function (e.g. ak.sum(a))

supported_opeq¶

supported_with_datetime¶

supported_with_pdarray¶

supported_with_r_datetime¶

supported_with_r_pdarray¶

supported_with_r_timedelta¶

supported_with_timedelta¶

to_pandas()[source]¶: Convert array to a pandas DatetimeIndex. Note: if the array size exceeds client.maxTransferBytes, a RuntimeError is raised.

See also

to_ndarray

unregister()[source]¶

Unregister this Datetime object in the arkouda server which was previously registered using register() and/or attached to using attach()

Raises:: RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

property week¶

property weekday¶

property weekofyear¶

property year¶

class arkouda.DatetimeAccessor(series)[source]¶

Bases: Properties

series¶

class arkouda.DiffAggregate[source]¶

A column in a GroupBy that has been differenced. Aggregation operations can be done on the result.

gb¶

GroupBy object, where the aggregation keys are values of column(s) of a dataframe.

Type:: arkouda.groupbyclass.GroupBy

values¶

A column to compute the difference on.

Type:: arkouda.series.Series.

all()¶

any()¶

argmax()¶

argmin()¶

count()¶

first()¶

max()¶

mean()¶

median()¶

min()¶

mode()¶

nunique()¶

prod()¶

std()¶

sum()¶

unique()¶

var()¶

xor()¶

class arkouda.Fields(values, names, MSB_left=True, pad='-', separator='', show_int=True)[source]¶

Bases: BitVector

An integer-backed representation of a set of named binary fields, e.g. flags.

Parameters:

values (pdarray or Strings) – The array of field values. If (u)int64, the values are used as-is for the binary representation of fields. If Strings, the values are converted to binary according to the mapping defined by the names and MSB_left arguments.
names (str or sequence of str) – The names of the fields, in order. A string will be treated as a list of single-character field names. Multi-character field names are allowed, but must be passed as a list or tuple and user must specify a separator.
MSB_left (bool) – Controls how field names are mapped to binary values. If True (default), the left-most field name corresponds to the most significant bit in the binary representation. If False, the left-most field name corresponds to the least significant bit.
pad (str) – Character to display when field is not present. Use empty string if no padding is desired.
separator (str) – Substring that separates fields. Used to parse input values (if ak.Strings) and to display output.
show_int (bool) – If True (default), display the integer value of the binary fields in output.

Returns:

fields – The array of field values

Return type:

Fields

Notes

This class is a thin wrapper around pdarray that mostly affects how values are displayed to the user. Operators and methods will typically treat this class like an int64 pdarray.

MSB_left = True¶

format(x)[source]¶: Format a single binary value as a string of named fields.

name = None¶

names¶

namewidth¶

opeq(other, op)[source]¶

pad¶

padchar = '-'¶

separator = ''¶

show_int = True¶

class arkouda.GROUPBY_REDUCTION_TYPES¶

frozenset() -> empty frozenset object frozenset(iterable) -> frozenset object

Build an immutable unordered collection of unique elements.

copy(*args, **kwargs)¶: Return a shallow copy of a set.

difference(*args, **kwargs)¶

Return the difference of two or more sets as a new set.

(i.e. all elements that are in this set but not the others.)

intersection(*args, **kwargs)¶

Return the intersection of two sets as a new set.

(i.e. all elements that are in both sets.)

isdisjoint(*args, **kwargs)¶: Return True if two sets have a null intersection.

issubset(*args, **kwargs)¶: Report whether another set contains this set.

issuperset(*args, **kwargs)¶: Report whether this set contains another set.

symmetric_difference(*args, **kwargs)¶

Return the symmetric difference of two sets as a new set.

(i.e. all elements that are in exactly one of the sets.)

union(*args, **kwargs)¶

Return the union of sets as a new set.

(i.e. all elements that are in either set.)

class arkouda.GroupBy[source]¶

Group an array or list of arrays by value, usually in preparation for aggregating the within-group values of another array.

Parameters:

keys ((list of) pdarray, Strings, or Categorical) – The array to group by value, or if list, the column arrays to group by row
assume_sorted (bool) – If True, assume keys is already sorted (Default: False)

nkeys¶

The number of key arrays (columns)

Type:: int

The length of the input array(s), i.e. number of rows

Type:: int

permutation¶

The permutation that sorts the keys array(s) by value (row)

Type:: pdarray

unique_keys¶

The unique values of the keys array(s), in grouped order

Type:: (list of) pdarray, Strings, or Categorical

ngroups¶

The length of the unique_keys array(s), i.e. number of groups

Type:: int

segments¶

The start index of each group in the grouped array(s)

Type:: pdarray

logger¶

Used for all logging operations

Type:: ArkoudaLogger

dropna¶

If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.

Type:: bool (default=True)

Raises:: TypeError – Raised if keys is a pdarray with a dtype other than int64

Notes

Integral pdarrays, Strings, and Categoricals are natively supported, but float64 and bool arrays are not.

For a user-defined class to be groupable, it must inherit from pdarray and define or overload the grouping API:

a ._get_grouping_keys() method that returns a list of pdarrays that can be (co)argsorted.

(Optional) a .group() method that returns the permutation that groups the array

If the input is a single array with a .group() method defined, method 2 will be used; otherwise, method 1 will be used.

AND(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise AND of values in each segment.

Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise AND reduction on each group.

Parameters:

values (pdarray, int64) – The values to group and reduce with AND

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise AND of values in segments corresponding to keys

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

OR(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise OR of values in each segment.

Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise OR reduction on each group.

Parameters:

values (pdarray, int64) – The values to group and reduce with OR

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise OR of values in segments corresponding to keys

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

Reductions(*args, **kwargs)¶

frozenset() -> empty frozenset object frozenset(iterable) -> frozenset object

Build an immutable unordered collection of unique elements.

XOR(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise XOR of values in each segment.

Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise XOR reduction on each group.

Parameters:

values (pdarray, int64) – The values to group and reduce with XOR

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise XOR of values in segments corresponding to keys

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

aggregate(values: groupable, operator: str, skipna: bool = True, ddof: int_scalars = 1) → Tuple[groupable, groupable][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and apply a reduction to each group’s values.

Parameters:

values (pdarray) – The values to group and reduce
operator (str) – The name of the reduction operator to use
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std

Returns:

unique_keys (groupable) – The unique keys, in grouped order
aggregates (groupable) – One aggregate value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if the requested operator is not supported for the values dtype

Examples

>>> keys = ak.arange(0, 10)
>>> vals = ak.linspace(-1, 1, 10)
>>> g = ak.GroupBy(keys)
>>> g.aggregate(vals, 'sum')
(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777768,
-0.55555555555555536, -0.33333333333333348, -0.11111111111111116,
0.11111111111111116, 0.33333333333333348, 0.55555555555555536, 0.77777777777777768,
1]))
>>> g.aggregate(vals, 'min')
(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777779,
-0.55555555555555558, -0.33333333333333337, -0.11111111111111116, 0.11111111111111116,
0.33333333333333326, 0.55555555555555536, 0.77777777777777768, 1]))

all(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and perform an “and” reduction on each group.

Parameters:

values (pdarray, bool) – The values to group and reduce with “and”

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

any(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and perform an “or” reduction on each group.

Parameters:

values (pdarray, bool) – The values to group and reduce with “or”

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

argmax(values: pdarray) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first maximum of each group’s values.

Parameters:

values (pdarray) – The values to group and find argmax

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argmaxima (pdarray, int64) – One index per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.argmax(b)
(array([2, 3, 4]), array([9, 3, 2]))

argmin(values: pdarray) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first minimum of each group’s values.

Parameters:

values (pdarray) – The values to group and find argmin

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argminima (pdarray, int64) – One index per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if argmin is not supported for the values dtype

Notes

The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.argmin(b)
(array([2, 3, 4]), array([5, 4, 2]))

attach(user_defined_name: str) → GroupBy[source]¶

Function to return a GroupBy object attached to the registered name in the arkouda server which was registered using register()

Parameters:: user_defined_name (str) – user defined name which GroupBy object was registered under
Returns:: The GroupBy object created by re-attaching to the corresponding server components
Return type:: GroupBy
Raises:: RegistrationError – if user_defined_name is not registered

broadcast(values: pdarray | Strings, permute: bool = True) → pdarray | Strings[source]¶

Fill each group’s segment with a constant value.

Parameters:

values (pdarray, Strings) – The values to put in each group’s segment
permute (bool) – If True (default), permute broadcast values back to the ordering of the original array on which GroupBy was called. If False, the broadcast values are grouped by value.

Returns:

The broadcasted values

Return type:

Raises:

TypeError – Raised if value is not a pdarray object
ValueError – Raised if the values array does not have one value per segment

Notes

This function is a sparse analog of np.broadcast. If a GroupBy object represents a sparse matrix (tensor), then this function takes a (dense) column vector and replicates each value to the non-zero elements in the corresponding row.

Examples

>>> a = ak.array([0, 1, 0, 1, 0])
>>> values = ak.array([3, 5])
>>> g = ak.GroupBy(a)
# By default, result is in original order
>>> g.broadcast(values)
array([3, 5, 3, 5, 3])
# With permute=False, result is in grouped order
>>> g.broadcast(values, permute=False)
array([3, 3, 3, 5, 5]
>>> a = ak.randint(1,5,10)
>>> a
array([3, 1, 4, 4, 4, 1, 3, 3, 2, 2])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.size()
>>> g.broadcast(counts > 2)
array([True False True True True False True True False False])
>>> g.broadcast(counts == 3)
array([True False True True True False True True False False])
>>> g.broadcast(counts < 4)
array([True True True True True True True True True True])

build_from_components(user_defined_name: str | None = None, **kwargs) → GroupBy[source]¶

function to build a new GroupBy object from component keys and permutation.

Parameters:

user_defined_name (str (Optional) Passing a name will init the new GroupBy) – and assign it the given name
kwargs (dict Dictionary of components required for rebuilding the GroupBy.) – Expected keys are “orig_keys”, “permutation”, “unique_keys”, and “segments”

Returns:

The GroupBy object created by using the given components

Return type:

count(values: pdarray) → Tuple[groupable, pdarray][source]¶

Count the number of elements in each group. NaN values will be excluded from the total.

Parameters:

values (pdarray) – The values to be count by group (excluding NaN values).

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears (excluding NaN values).

Examples

>>> a = ak.array([1, 0, -1, 1, 0, -1])
>>> a
array([1 0 -1 1 0 -1])
>>> b = ak.array([1, np.nan, -1, np.nan, np.nan, -1], dtype = "float64")
>>> b
array([1.00000000000000000 nan -1.00000000000000000 nan nan -1.00000000000000000])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.count(b)
>>> keys
array([-1 0 1])
>>> counts
array([2 0 1])

first(values: groupable_element_type) → Tuple[groupable, groupable_element_type][source]¶

First value in each group.

Parameters:

values (pdarray-like) – The values from which to take the first of each group

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The first value of each group

from_return_msg(rep_msg)[source]¶

head(values: groupable_element_type, n: int = 5, return_indices: bool = True) → Tuple[groupable, groupable_element_type][source]¶

Return the first n values from each group.

Parameters:

values ((list of) pdarray-like) – The values from which to select, according to their group membership.
n (int, optional, default = 5) – Maximum number of items to return for each group. If the number of values in a group is less than n, all the values from that group will be returned.
return_indices (bool, default False) – If True, return the indices of the sampled values. Otherwise, return the selected values.

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The first n items of each group. If return_indices is True, the result are indices. O.W. the result are values.

Examples

>>> a = ak.arange(10) %3
>>> a
array([0 1 2 0 1 2 0 1 2 0])
>>> v = ak.arange(10)
>>> v
array([0 1 2 3 4 5 6 7 8 9])
>>> g = GroupBy(a)
>>> unique_keys, idx = g.head(v, 2, return_indices=True)
>>> _, values = g.head(v, 2, return_indices=False)
>>> unique_keys
array([0 1 2])
>>> idx
array([0 3 1 4 2 5])
>>> values
array([0 3 1 4 2 5])

>>> v2 =  -2 * ak.arange(10)
>>> v2
array([0 -2 -4 -6 -8 -10 -12 -14 -16 -18])
>>> _, idx2 = g.head(v2, 2, return_indices=True)
>>> _, values2 = g.head(v2, 2, return_indices=False)
>>> idx2
array([0 3 1 4 2 5])
>>> values2
array([0 -6 -2 -8 -4 -10])

is_registered() → bool[source]¶

Return True if the object is contained in the registry

Returns:: Indicates if the object is contained in the registry
Return type:: bool
Raises:: RegistrationError – Raised if there’s a server-side error or a mismatch of registered components

Notes

Objects registered with the server are immune to deletion until they are unregistered.

max(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the maximum of each group’s values.

Parameters:

values (pdarray) – The values to group and find maxima
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_maxima (pdarray) – One maximum per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if max is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if max is not supported for the values dtype

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.max(b)
(array([2, 3, 4]), array([4, 4, 3]))

mean(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the mean of each group’s values.

Parameters:

values (pdarray) – The values to group and average
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_means (pdarray, float64) – One mean value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.mean(b)
(array([2, 3, 4]), array([2.6666666666666665, 2.7999999999999998, 3]))

median(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the median of each group’s values.

Parameters:

values (pdarray) – The values to group and find median
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_medians (pdarray, float64) – One median value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

Examples

>>> a = ak.randint(1,5,9)
>>> a
array([4 1 4 3 2 2 2 3 3])
>>> g = ak.GroupBy(a)
>>> g.keys
array([4 1 4 3 2 2 2 3 3])
>>> b = ak.linspace(-5,5,9)
>>> b
array([-5 -3.75 -2.5 -1.25 0 1.25 2.5 3.75 5])
>>> g.median(b)
(array([1 2 3 4]), array([-3.75 1.25 3.75 -3.75]))

min(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the minimum of each group’s values.

Parameters:

values (pdarray) – The values to group and find minima
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_minima (pdarray) – One minimum per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if min is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if min is not supported for the values dtype

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.min(b)
(array([2, 3, 4]), array([1, 1, 3]))

mode(values: groupable) → Tuple[groupable, groupable][source]¶

Most common value in each group. If a group is multi-modal, return the modal value that occurs first.

Parameters:

values ((list of) pdarray-like) – The values from which to take the mode of each group

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) pdarray-like) – The most common value of each group

most_common(values)[source]¶: (Deprecated) See GroupBy.mode().

nunique(values: groupable) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the number of unique values in each group.

Parameters:

values (pdarray, int64) – The values to group and find unique values

Returns:

unique_keys (groupable) – The unique keys, in grouped order
group_nunique (groupable) – Number of unique values per unique key in the GroupBy instance

Raises:

TypeError – Raised if the dtype(s) of values array(s) does/do not support the nunique method
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if nunique is not supported for the values dtype

Examples

>>> data = ak.array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4])
>>> data
array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4])
>>> labels = ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4])
>>> labels
ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4])
>>> g = ak.GroupBy(labels)
>>> g.keys
ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4])
>>> g.nunique(data)
array([1,2,3,4]), array([2, 2, 3, 1])
#    Group (1,1,1) has values [3,4,3] -> there are 2 unique values 3&4
#    Group (2,2,2) has values [1,1,4] -> 2 unique values 1&4
#    Group (3,3,3) has values [3,4,1] -> 3 unique values
#    Group (4) has values [4] -> 1 unique value

objType(*args, **kwargs)¶

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

prod(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the product of each group’s values.

Parameters:

values (pdarray) – The values to group and multiply
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_products (pdarray, float64) – One product per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if prod is not supported for the values dtype

Notes

The return dtype is always float64.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.prod(b)
(array([2, 3, 4]), array([12, 108.00000000000003, 8.9999999999999982]))

register(user_defined_name: str) → GroupBy[source]¶

Parameters:

user_defined_name (str) – user defined name the GroupBy is to be registered under, this will be the root name for underlying components

Returns:

The same GroupBy which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different GroupBys with the same name.

Return type:

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the GroupBy with the user_defined_name

Notes

Objects registered with the server are immune to deletion until they are unregistered.

sample(values: groupable, n=None, frac=None, replace=False, weights=None, random_state=None, return_indices=False, permute_samples=False)[source]¶

Return a random sample from each group. You can either specify the number of elements or the fraction of elements to be sampled. random_state can be used for reproducibility

Parameters:

values ((list of) pdarray-like) – The values from which to sample, according to their group membership.
n (int, optional) – Number of items to return for each group. Cannot be used with frac and must be no larger than the smallest group unless replace is True. Default is one if frac is None.
frac (float, optional) – Fraction of items to return. Cannot be used with n.
replace (bool, default False) – Allow or disallow sampling of the value more than once.
weights (pdarray, optional) – Default None results in equal probability weighting. If passed a pdarray, then values must have the same length as the groupby keys and will be used as sampling probabilities after normalization within each group. Weights must be non-negative with at least one positive element within each group.
random_state (int or ak.random.Generator, optional) – If int, seed for random number generator. If ak.random.Generator, use as given.
return_indices (bool, default False) – if True, return the indices of the sampled values. Otherwise, return the sample values.
permute_samples (bool, default False) – if True, return permute the samples according to group Otherwise, keep samples in original order.

Returns:

if return_indices is True, return the indices of the sampled values. Otherwise, return the sample values.

Return type:

size() → Tuple[groupable, pdarray][source]¶

Count the number of elements in each group, i.e. the number of times each key appears. This counts the total number of rows (including NaN values).

Parameters:

none

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears

See also

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 2, 3, 1, 2, 4, 3, 4, 3, 4])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.size()
>>> keys
array([1, 2, 3, 4])
>>> counts
array([1, 2, 4, 3])

std(values: pdarray, skipna: bool = True, ddof: int_scalars = 1) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the standard deviation of each group’s values.

Parameters:

values (pdarray) – The values to group and find standard deviation
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_stds (pdarray, float64) – One std value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

The standard deviation is the square root of the average of the squared deviations from the mean, i.e., std = sqrt(mean((x - x.mean())**2)).

The average squared deviation is normally calculated as x.sum() / N, where N = len(x). If, however, ddof is specified, the divisor N - ddof is used instead. In standard statistical practice, ddof=1 provides an unbiased estimator of the variance of the infinite population. ddof=0 provides a maximum likelihood estimate of the variance for normally distributed variables. The standard deviation computed in this function is the square root of the estimated variance, so even with ddof=1, it will not be an unbiased estimate of the standard deviation per se.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.std(b)
(array([2 3 4]), array([1.5275252316519465 1.0954451150103321 0]))

sum(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and sum each group’s values.

Parameters:

values (pdarray) – The values to group and sum
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_sums (pdarray) – One sum per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The grouped sum of a boolean pdarray returns integers.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.sum(b)
(array([2, 3, 4]), array([8, 14, 6]))

tail(values: groupable_element_type, n: int = 5, return_indices: bool = True) → Tuple[groupable, groupable_element_type][source]¶

Return the last n values from each group.

Parameters:

values ((list of) pdarray-like) – The values from which to select, according to their group membership.
n (int, optional, default = 5) – Maximum number of items to return for each group. If the number of values in a group is less than n, all the values from that group will be returned.
return_indices (bool, default False) – If True, return the indices of the sampled values. Otherwise, return the selected values.

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The last n items of each group. If return_indices is True, the result are indices. O.W. the result are values.

Examples

>>> a = ak.arange(10) %3
>>> a
array([0 1 2 0 1 2 0 1 2 0])
>>> v = ak.arange(10)
>>> v
array([0 1 2 3 4 5 6 7 8 9])
>>> g = GroupBy(a)
>>> unique_keys, idx = g.tail(v, 2, return_indices=True)
>>> _, values = g.tail(v, 2, return_indices=False)
>>> unique_keys
array([0 1 2])
>>> idx
array([6 9 4 7 5 8])
>>> values
array([6 9 4 7 5 8])

>>> v2 =  -2 * ak.arange(10)
>>> v2
array([0 -2 -4 -6 -8 -10 -12 -14 -16 -18])
>>> _, idx2 = g.tail(v2, 2, return_indices=True)
>>> _, values2 = g.tail(v2, 2, return_indices=False)
>>> idx2
array([6 9 4 7 5 8])
>>> values2
array([-12 -18 -8 -14 -10 -16])

to_hdf(prefix_path, dataset='groupby', mode='truncate', file_type='distribute')[source]¶

Save the GroupBy to HDF5. The result is a collection of HDF5 files, one file per locale of the arkouda server, where each filename starts with prefix_path.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files will share
dataset (str) – Name prefix for saved data within the HDF5 file
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, add data as a new column to existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.

Returns:

None
GroupBy is not currently supported by Parquet

unique(values: groupable)[source]¶

Return the set of unique values in each group, as a SegArray.

Parameters:

values ((list of) pdarray-like) – The values to unique

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) SegArray) – The unique values of each group

Raises:

TypeError – Raised if values is or contains Strings or Categorical

unregister()[source]¶

Unregister this GroupBy object in the arkouda server which was previously registered using register() and/or attached to using attach()

Raises:: RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister

Notes

Objects registered with the server are immune to deletion until they are unregistered.

unregister_groupby_by_name(user_defined_name: str) → None[source]¶

Function to unregister GroupBy object by name which was registered with the arkouda server via register()

Parameters:

user_defined_name (str) – Name under which the GroupBy object was registered

Raises:

TypeError – if user_defined_name is not a string
RegistrationError – if there is an issue attempting to unregister any underlying components

update_hdf(prefix_path: str, dataset: str = 'groupby', repack: bool = True)[source]¶

var(values: pdarray, skipna: bool = True, ddof: int_scalars = 1) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the variance of each group’s values.

Parameters:

values (pdarray) – The values to group and find variance
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_vars (pdarray, float64) – One var value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

The variance is the average of the squared deviations from the mean, i.e., var = mean((x - x.mean())**2).

The mean is normally calculated as x.sum() / N, where N = len(x). If, however, ddof is specified, the divisor N - ddof is used instead. In standard statistical practice, ddof=1 provides an unbiased estimator of the variance of a hypothetical infinite population. ddof=0 provides a maximum likelihood estimate of the variance for normally distributed variables.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.var(b)
(array([2 3 4]), array([2.333333333333333 1.2 0]))

class arkouda.GroupBy[source]¶

Group an array or list of arrays by value, usually in preparation for aggregating the within-group values of another array.

Parameters:

keys ((list of) pdarray, Strings, or Categorical) – The array to group by value, or if list, the column arrays to group by row
assume_sorted (bool) – If True, assume keys is already sorted (Default: False)

nkeys¶

The number of key arrays (columns)

Type:: int

The length of the input array(s), i.e. number of rows

Type:: int

permutation¶

The permutation that sorts the keys array(s) by value (row)

Type:: pdarray

unique_keys¶

The unique values of the keys array(s), in grouped order

Type:: (list of) pdarray, Strings, or Categorical

ngroups¶

The length of the unique_keys array(s), i.e. number of groups

Type:: int

segments¶

The start index of each group in the grouped array(s)

Type:: pdarray

logger¶

Used for all logging operations

Type:: ArkoudaLogger

dropna¶

If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.

Type:: bool (default=True)

Raises:: TypeError – Raised if keys is a pdarray with a dtype other than int64

Notes

Integral pdarrays, Strings, and Categoricals are natively supported, but float64 and bool arrays are not.

For a user-defined class to be groupable, it must inherit from pdarray and define or overload the grouping API:

a ._get_grouping_keys() method that returns a list of pdarrays that can be (co)argsorted.

(Optional) a .group() method that returns the permutation that groups the array

If the input is a single array with a .group() method defined, method 2 will be used; otherwise, method 1 will be used.

AND(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise AND of values in each segment.

Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise AND reduction on each group.

Parameters:

values (pdarray, int64) – The values to group and reduce with AND

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise AND of values in segments corresponding to keys

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

OR(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise OR of values in each segment.

Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise OR reduction on each group.

Parameters:

values (pdarray, int64) – The values to group and reduce with OR

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise OR of values in segments corresponding to keys

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

Reductions(*args, **kwargs)¶

frozenset() -> empty frozenset object frozenset(iterable) -> frozenset object

Build an immutable unordered collection of unique elements.

XOR(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise XOR of values in each segment.

Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise XOR reduction on each group.

Parameters:

values (pdarray, int64) – The values to group and reduce with XOR

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise XOR of values in segments corresponding to keys

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

aggregate(values: groupable, operator: str, skipna: bool = True, ddof: int_scalars = 1) → Tuple[groupable, groupable][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and apply a reduction to each group’s values.

Parameters:

values (pdarray) – The values to group and reduce
operator (str) – The name of the reduction operator to use
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std

Returns:

unique_keys (groupable) – The unique keys, in grouped order
aggregates (groupable) – One aggregate value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if the requested operator is not supported for the values dtype

Examples

>>> keys = ak.arange(0, 10)
>>> vals = ak.linspace(-1, 1, 10)
>>> g = ak.GroupBy(keys)
>>> g.aggregate(vals, 'sum')
(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777768,
-0.55555555555555536, -0.33333333333333348, -0.11111111111111116,
0.11111111111111116, 0.33333333333333348, 0.55555555555555536, 0.77777777777777768,
1]))
>>> g.aggregate(vals, 'min')
(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777779,
-0.55555555555555558, -0.33333333333333337, -0.11111111111111116, 0.11111111111111116,
0.33333333333333326, 0.55555555555555536, 0.77777777777777768, 1]))

all(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and perform an “and” reduction on each group.

Parameters:

values (pdarray, bool) – The values to group and reduce with “and”

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

any(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and perform an “or” reduction on each group.

Parameters:

values (pdarray, bool) – The values to group and reduce with “or”

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

argmax(values: pdarray) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first maximum of each group’s values.

Parameters:

values (pdarray) – The values to group and find argmax

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argmaxima (pdarray, int64) – One index per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.argmax(b)
(array([2, 3, 4]), array([9, 3, 2]))

argmin(values: pdarray) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first minimum of each group’s values.

Parameters:

values (pdarray) – The values to group and find argmin

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argminima (pdarray, int64) – One index per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if argmin is not supported for the values dtype

Notes

The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.argmin(b)
(array([2, 3, 4]), array([5, 4, 2]))

attach(user_defined_name: str) → GroupBy[source]¶

Function to return a GroupBy object attached to the registered name in the arkouda server which was registered using register()

Parameters:: user_defined_name (str) – user defined name which GroupBy object was registered under
Returns:: The GroupBy object created by re-attaching to the corresponding server components
Return type:: GroupBy
Raises:: RegistrationError – if user_defined_name is not registered

broadcast(values: pdarray | Strings, permute: bool = True) → pdarray | Strings[source]¶

Fill each group’s segment with a constant value.

Parameters:

values (pdarray, Strings) – The values to put in each group’s segment
permute (bool) – If True (default), permute broadcast values back to the ordering of the original array on which GroupBy was called. If False, the broadcast values are grouped by value.

Returns:

The broadcasted values

Return type:

Raises:

TypeError – Raised if value is not a pdarray object
ValueError – Raised if the values array does not have one value per segment

Notes

Examples

>>> a = ak.array([0, 1, 0, 1, 0])
>>> values = ak.array([3, 5])
>>> g = ak.GroupBy(a)
# By default, result is in original order
>>> g.broadcast(values)
array([3, 5, 3, 5, 3])
# With permute=False, result is in grouped order
>>> g.broadcast(values, permute=False)
array([3, 3, 3, 5, 5]
>>> a = ak.randint(1,5,10)
>>> a
array([3, 1, 4, 4, 4, 1, 3, 3, 2, 2])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.size()
>>> g.broadcast(counts > 2)
array([True False True True True False True True False False])
>>> g.broadcast(counts == 3)
array([True False True True True False True True False False])
>>> g.broadcast(counts < 4)
array([True True True True True True True True True True])

build_from_components(user_defined_name: str | None = None, **kwargs) → GroupBy[source]¶

function to build a new GroupBy object from component keys and permutation.

Parameters:

user_defined_name (str (Optional) Passing a name will init the new GroupBy) – and assign it the given name
kwargs (dict Dictionary of components required for rebuilding the GroupBy.) – Expected keys are “orig_keys”, “permutation”, “unique_keys”, and “segments”

Returns:

The GroupBy object created by using the given components

Return type:

count(values: pdarray) → Tuple[groupable, pdarray][source]¶

Count the number of elements in each group. NaN values will be excluded from the total.

Parameters:

values (pdarray) – The values to be count by group (excluding NaN values).

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears (excluding NaN values).

Examples

>>> a = ak.array([1, 0, -1, 1, 0, -1])
>>> a
array([1 0 -1 1 0 -1])
>>> b = ak.array([1, np.nan, -1, np.nan, np.nan, -1], dtype = "float64")
>>> b
array([1.00000000000000000 nan -1.00000000000000000 nan nan -1.00000000000000000])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.count(b)
>>> keys
array([-1 0 1])
>>> counts
array([2 0 1])

first(values: groupable_element_type) → Tuple[groupable, groupable_element_type][source]¶

First value in each group.

Parameters:

values (pdarray-like) – The values from which to take the first of each group

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The first value of each group

from_return_msg(rep_msg)[source]¶

head(values: groupable_element_type, n: int = 5, return_indices: bool = True) → Tuple[groupable, groupable_element_type][source]¶

Return the first n values from each group.

Parameters:

values ((list of) pdarray-like) – The values from which to select, according to their group membership.
n (int, optional, default = 5) – Maximum number of items to return for each group. If the number of values in a group is less than n, all the values from that group will be returned.
return_indices (bool, default False) – If True, return the indices of the sampled values. Otherwise, return the selected values.

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The first n items of each group. If return_indices is True, the result are indices. O.W. the result are values.

Examples

>>> a = ak.arange(10) %3
>>> a
array([0 1 2 0 1 2 0 1 2 0])
>>> v = ak.arange(10)
>>> v
array([0 1 2 3 4 5 6 7 8 9])
>>> g = GroupBy(a)
>>> unique_keys, idx = g.head(v, 2, return_indices=True)
>>> _, values = g.head(v, 2, return_indices=False)
>>> unique_keys
array([0 1 2])
>>> idx
array([0 3 1 4 2 5])
>>> values
array([0 3 1 4 2 5])

>>> v2 =  -2 * ak.arange(10)
>>> v2
array([0 -2 -4 -6 -8 -10 -12 -14 -16 -18])
>>> _, idx2 = g.head(v2, 2, return_indices=True)
>>> _, values2 = g.head(v2, 2, return_indices=False)
>>> idx2
array([0 3 1 4 2 5])
>>> values2
array([0 -6 -2 -8 -4 -10])

is_registered() → bool[source]¶

Return True if the object is contained in the registry

Returns:: Indicates if the object is contained in the registry
Return type:: bool
Raises:: RegistrationError – Raised if there’s a server-side error or a mismatch of registered components

Notes

Objects registered with the server are immune to deletion until they are unregistered.

max(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the maximum of each group’s values.

Parameters:

values (pdarray) – The values to group and find maxima
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_maxima (pdarray) – One maximum per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if max is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if max is not supported for the values dtype

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.max(b)
(array([2, 3, 4]), array([4, 4, 3]))

mean(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the mean of each group’s values.

Parameters:

values (pdarray) – The values to group and average
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_means (pdarray, float64) – One mean value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.mean(b)
(array([2, 3, 4]), array([2.6666666666666665, 2.7999999999999998, 3]))

median(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the median of each group’s values.

Parameters:

values (pdarray) – The values to group and find median
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_medians (pdarray, float64) – One median value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

Examples

>>> a = ak.randint(1,5,9)
>>> a
array([4 1 4 3 2 2 2 3 3])
>>> g = ak.GroupBy(a)
>>> g.keys
array([4 1 4 3 2 2 2 3 3])
>>> b = ak.linspace(-5,5,9)
>>> b
array([-5 -3.75 -2.5 -1.25 0 1.25 2.5 3.75 5])
>>> g.median(b)
(array([1 2 3 4]), array([-3.75 1.25 3.75 -3.75]))

min(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the minimum of each group’s values.

Parameters:

values (pdarray) – The values to group and find minima
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_minima (pdarray) – One minimum per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if min is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if min is not supported for the values dtype

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.min(b)
(array([2, 3, 4]), array([1, 1, 3]))

mode(values: groupable) → Tuple[groupable, groupable][source]¶

Most common value in each group. If a group is multi-modal, return the modal value that occurs first.

Parameters:

values ((list of) pdarray-like) – The values from which to take the mode of each group

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) pdarray-like) – The most common value of each group

most_common(values)[source]¶: (Deprecated) See GroupBy.mode().

nunique(values: groupable) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the number of unique values in each group.

Parameters:

values (pdarray, int64) – The values to group and find unique values

Returns:

unique_keys (groupable) – The unique keys, in grouped order
group_nunique (groupable) – Number of unique values per unique key in the GroupBy instance

Raises:

TypeError – Raised if the dtype(s) of values array(s) does/do not support the nunique method
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if nunique is not supported for the values dtype

Examples

>>> data = ak.array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4])
>>> data
array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4])
>>> labels = ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4])
>>> labels
ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4])
>>> g = ak.GroupBy(labels)
>>> g.keys
ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4])
>>> g.nunique(data)
array([1,2,3,4]), array([2, 2, 3, 1])
#    Group (1,1,1) has values [3,4,3] -> there are 2 unique values 3&4
#    Group (2,2,2) has values [1,1,4] -> 2 unique values 1&4
#    Group (3,3,3) has values [3,4,1] -> 3 unique values
#    Group (4) has values [4] -> 1 unique value

objType(*args, **kwargs)¶

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

prod(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the product of each group’s values.

Parameters:

values (pdarray) – The values to group and multiply
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_products (pdarray, float64) – One product per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if prod is not supported for the values dtype

Notes

The return dtype is always float64.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.prod(b)
(array([2, 3, 4]), array([12, 108.00000000000003, 8.9999999999999982]))

register(user_defined_name: str) → GroupBy[source]¶

Parameters:

user_defined_name (str) – user defined name the GroupBy is to be registered under, this will be the root name for underlying components

Returns:

Return type:

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the GroupBy with the user_defined_name

Notes

Objects registered with the server are immune to deletion until they are unregistered.

sample(values: groupable, n=None, frac=None, replace=False, weights=None, random_state=None, return_indices=False, permute_samples=False)[source]¶

Return a random sample from each group. You can either specify the number of elements or the fraction of elements to be sampled. random_state can be used for reproducibility

Parameters:

values ((list of) pdarray-like) – The values from which to sample, according to their group membership.
n (int, optional) – Number of items to return for each group. Cannot be used with frac and must be no larger than the smallest group unless replace is True. Default is one if frac is None.
frac (float, optional) – Fraction of items to return. Cannot be used with n.
replace (bool, default False) – Allow or disallow sampling of the value more than once.
weights (pdarray, optional) – Default None results in equal probability weighting. If passed a pdarray, then values must have the same length as the groupby keys and will be used as sampling probabilities after normalization within each group. Weights must be non-negative with at least one positive element within each group.
random_state (int or ak.random.Generator, optional) – If int, seed for random number generator. If ak.random.Generator, use as given.
return_indices (bool, default False) – if True, return the indices of the sampled values. Otherwise, return the sample values.
permute_samples (bool, default False) – if True, return permute the samples according to group Otherwise, keep samples in original order.

Returns:

if return_indices is True, return the indices of the sampled values. Otherwise, return the sample values.

Return type:

size() → Tuple[groupable, pdarray][source]¶

Count the number of elements in each group, i.e. the number of times each key appears. This counts the total number of rows (including NaN values).

Parameters:

none

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears

See also

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 2, 3, 1, 2, 4, 3, 4, 3, 4])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.size()
>>> keys
array([1, 2, 3, 4])
>>> counts
array([1, 2, 4, 3])

std(values: pdarray, skipna: bool = True, ddof: int_scalars = 1) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the standard deviation of each group’s values.

Parameters:

values (pdarray) – The values to group and find standard deviation
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_stds (pdarray, float64) – One std value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

The standard deviation is the square root of the average of the squared deviations from the mean, i.e., std = sqrt(mean((x - x.mean())**2)).

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.std(b)
(array([2 3 4]), array([1.5275252316519465 1.0954451150103321 0]))

sum(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and sum each group’s values.

Parameters:

values (pdarray) – The values to group and sum
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_sums (pdarray) – One sum per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The grouped sum of a boolean pdarray returns integers.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.sum(b)
(array([2, 3, 4]), array([8, 14, 6]))

tail(values: groupable_element_type, n: int = 5, return_indices: bool = True) → Tuple[groupable, groupable_element_type][source]¶

Return the last n values from each group.

Parameters:

values ((list of) pdarray-like) – The values from which to select, according to their group membership.
n (int, optional, default = 5) – Maximum number of items to return for each group. If the number of values in a group is less than n, all the values from that group will be returned.
return_indices (bool, default False) – If True, return the indices of the sampled values. Otherwise, return the selected values.

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The last n items of each group. If return_indices is True, the result are indices. O.W. the result are values.

Examples

>>> a = ak.arange(10) %3
>>> a
array([0 1 2 0 1 2 0 1 2 0])
>>> v = ak.arange(10)
>>> v
array([0 1 2 3 4 5 6 7 8 9])
>>> g = GroupBy(a)
>>> unique_keys, idx = g.tail(v, 2, return_indices=True)
>>> _, values = g.tail(v, 2, return_indices=False)
>>> unique_keys
array([0 1 2])
>>> idx
array([6 9 4 7 5 8])
>>> values
array([6 9 4 7 5 8])

>>> v2 =  -2 * ak.arange(10)
>>> v2
array([0 -2 -4 -6 -8 -10 -12 -14 -16 -18])
>>> _, idx2 = g.tail(v2, 2, return_indices=True)
>>> _, values2 = g.tail(v2, 2, return_indices=False)
>>> idx2
array([6 9 4 7 5 8])
>>> values2
array([-12 -18 -8 -14 -10 -16])

to_hdf(prefix_path, dataset='groupby', mode='truncate', file_type='distribute')[source]¶

Save the GroupBy to HDF5. The result is a collection of HDF5 files, one file per locale of the arkouda server, where each filename starts with prefix_path.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files will share
dataset (str) – Name prefix for saved data within the HDF5 file
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, add data as a new column to existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.

Returns:

None
GroupBy is not currently supported by Parquet

unique(values: groupable)[source]¶

Return the set of unique values in each group, as a SegArray.

Parameters:

values ((list of) pdarray-like) – The values to unique

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) SegArray) – The unique values of each group

Raises:

TypeError – Raised if values is or contains Strings or Categorical

unregister()[source]¶

Unregister this GroupBy object in the arkouda server which was previously registered using register() and/or attached to using attach()

Raises:: RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister

Notes

Objects registered with the server are immune to deletion until they are unregistered.

unregister_groupby_by_name(user_defined_name: str) → None[source]¶

Function to unregister GroupBy object by name which was registered with the arkouda server via register()

Parameters:

user_defined_name (str) – Name under which the GroupBy object was registered

Raises:

TypeError – if user_defined_name is not a string
RegistrationError – if there is an issue attempting to unregister any underlying components

update_hdf(prefix_path: str, dataset: str = 'groupby', repack: bool = True)[source]¶

var(values: pdarray, skipna: bool = True, ddof: int_scalars = 1) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the variance of each group’s values.

Parameters:

values (pdarray) – The values to group and find variance
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_vars (pdarray, float64) – One var value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

The variance is the average of the squared deviations from the mean, i.e., var = mean((x - x.mean())**2).

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.var(b)
(array([2 3 4]), array([2.333333333333333 1.2 0]))

class arkouda.GroupBy[source]¶

Group an array or list of arrays by value, usually in preparation for aggregating the within-group values of another array.

Parameters:

keys ((list of) pdarray, Strings, or Categorical) – The array to group by value, or if list, the column arrays to group by row
assume_sorted (bool) – If True, assume keys is already sorted (Default: False)

nkeys¶

The number of key arrays (columns)

Type:: int

The length of the input array(s), i.e. number of rows

Type:: int

permutation¶

The permutation that sorts the keys array(s) by value (row)

Type:: pdarray

unique_keys¶

The unique values of the keys array(s), in grouped order

Type:: (list of) pdarray, Strings, or Categorical

ngroups¶

The length of the unique_keys array(s), i.e. number of groups

Type:: int

segments¶

The start index of each group in the grouped array(s)

Type:: pdarray

logger¶

Used for all logging operations

Type:: ArkoudaLogger

dropna¶

If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.

Type:: bool (default=True)

Raises:: TypeError – Raised if keys is a pdarray with a dtype other than int64

Notes

Integral pdarrays, Strings, and Categoricals are natively supported, but float64 and bool arrays are not.

For a user-defined class to be groupable, it must inherit from pdarray and define or overload the grouping API:

a ._get_grouping_keys() method that returns a list of pdarrays that can be (co)argsorted.

(Optional) a .group() method that returns the permutation that groups the array

If the input is a single array with a .group() method defined, method 2 will be used; otherwise, method 1 will be used.

AND(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise AND of values in each segment.

Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise AND reduction on each group.

Parameters:

values (pdarray, int64) – The values to group and reduce with AND

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise AND of values in segments corresponding to keys

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

OR(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise OR of values in each segment.

Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise OR reduction on each group.

Parameters:

values (pdarray, int64) – The values to group and reduce with OR

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise OR of values in segments corresponding to keys

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

Reductions(*args, **kwargs)¶

frozenset() -> empty frozenset object frozenset(iterable) -> frozenset object

Build an immutable unordered collection of unique elements.

XOR(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise XOR of values in each segment.

Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise XOR reduction on each group.

Parameters:

values (pdarray, int64) – The values to group and reduce with XOR

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise XOR of values in segments corresponding to keys

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

aggregate(values: groupable, operator: str, skipna: bool = True, ddof: int_scalars = 1) → Tuple[groupable, groupable][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and apply a reduction to each group’s values.

Parameters:

values (pdarray) – The values to group and reduce
operator (str) – The name of the reduction operator to use
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std

Returns:

unique_keys (groupable) – The unique keys, in grouped order
aggregates (groupable) – One aggregate value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if the requested operator is not supported for the values dtype

Examples

>>> keys = ak.arange(0, 10)
>>> vals = ak.linspace(-1, 1, 10)
>>> g = ak.GroupBy(keys)
>>> g.aggregate(vals, 'sum')
(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777768,
-0.55555555555555536, -0.33333333333333348, -0.11111111111111116,
0.11111111111111116, 0.33333333333333348, 0.55555555555555536, 0.77777777777777768,
1]))
>>> g.aggregate(vals, 'min')
(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777779,
-0.55555555555555558, -0.33333333333333337, -0.11111111111111116, 0.11111111111111116,
0.33333333333333326, 0.55555555555555536, 0.77777777777777768, 1]))

all(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and perform an “and” reduction on each group.

Parameters:

values (pdarray, bool) – The values to group and reduce with “and”

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

any(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and perform an “or” reduction on each group.

Parameters:

values (pdarray, bool) – The values to group and reduce with “or”

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

argmax(values: pdarray) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first maximum of each group’s values.

Parameters:

values (pdarray) – The values to group and find argmax

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argmaxima (pdarray, int64) – One index per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.argmax(b)
(array([2, 3, 4]), array([9, 3, 2]))

argmin(values: pdarray) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first minimum of each group’s values.

Parameters:

values (pdarray) – The values to group and find argmin

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argminima (pdarray, int64) – One index per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if argmin is not supported for the values dtype

Notes

The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.argmin(b)
(array([2, 3, 4]), array([5, 4, 2]))

attach(user_defined_name: str) → GroupBy[source]¶

Function to return a GroupBy object attached to the registered name in the arkouda server which was registered using register()

Parameters:: user_defined_name (str) – user defined name which GroupBy object was registered under
Returns:: The GroupBy object created by re-attaching to the corresponding server components
Return type:: GroupBy
Raises:: RegistrationError – if user_defined_name is not registered

broadcast(values: pdarray | Strings, permute: bool = True) → pdarray | Strings[source]¶

Fill each group’s segment with a constant value.

Parameters:

values (pdarray, Strings) – The values to put in each group’s segment
permute (bool) – If True (default), permute broadcast values back to the ordering of the original array on which GroupBy was called. If False, the broadcast values are grouped by value.

Returns:

The broadcasted values

Return type:

Raises:

TypeError – Raised if value is not a pdarray object
ValueError – Raised if the values array does not have one value per segment

Notes

Examples

>>> a = ak.array([0, 1, 0, 1, 0])
>>> values = ak.array([3, 5])
>>> g = ak.GroupBy(a)
# By default, result is in original order
>>> g.broadcast(values)
array([3, 5, 3, 5, 3])
# With permute=False, result is in grouped order
>>> g.broadcast(values, permute=False)
array([3, 3, 3, 5, 5]
>>> a = ak.randint(1,5,10)
>>> a
array([3, 1, 4, 4, 4, 1, 3, 3, 2, 2])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.size()
>>> g.broadcast(counts > 2)
array([True False True True True False True True False False])
>>> g.broadcast(counts == 3)
array([True False True True True False True True False False])
>>> g.broadcast(counts < 4)
array([True True True True True True True True True True])

build_from_components(user_defined_name: str | None = None, **kwargs) → GroupBy[source]¶

function to build a new GroupBy object from component keys and permutation.

Parameters:

user_defined_name (str (Optional) Passing a name will init the new GroupBy) – and assign it the given name
kwargs (dict Dictionary of components required for rebuilding the GroupBy.) – Expected keys are “orig_keys”, “permutation”, “unique_keys”, and “segments”

Returns:

The GroupBy object created by using the given components

Return type:

count(values: pdarray) → Tuple[groupable, pdarray][source]¶

Count the number of elements in each group. NaN values will be excluded from the total.

Parameters:

values (pdarray) – The values to be count by group (excluding NaN values).

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears (excluding NaN values).

Examples

>>> a = ak.array([1, 0, -1, 1, 0, -1])
>>> a
array([1 0 -1 1 0 -1])
>>> b = ak.array([1, np.nan, -1, np.nan, np.nan, -1], dtype = "float64")
>>> b
array([1.00000000000000000 nan -1.00000000000000000 nan nan -1.00000000000000000])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.count(b)
>>> keys
array([-1 0 1])
>>> counts
array([2 0 1])

first(values: groupable_element_type) → Tuple[groupable, groupable_element_type][source]¶

First value in each group.

Parameters:

values (pdarray-like) – The values from which to take the first of each group

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The first value of each group

from_return_msg(rep_msg)[source]¶

head(values: groupable_element_type, n: int = 5, return_indices: bool = True) → Tuple[groupable, groupable_element_type][source]¶

Return the first n values from each group.

Parameters:

values ((list of) pdarray-like) – The values from which to select, according to their group membership.
n (int, optional, default = 5) – Maximum number of items to return for each group. If the number of values in a group is less than n, all the values from that group will be returned.
return_indices (bool, default False) – If True, return the indices of the sampled values. Otherwise, return the selected values.

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The first n items of each group. If return_indices is True, the result are indices. O.W. the result are values.

Examples

>>> a = ak.arange(10) %3
>>> a
array([0 1 2 0 1 2 0 1 2 0])
>>> v = ak.arange(10)
>>> v
array([0 1 2 3 4 5 6 7 8 9])
>>> g = GroupBy(a)
>>> unique_keys, idx = g.head(v, 2, return_indices=True)
>>> _, values = g.head(v, 2, return_indices=False)
>>> unique_keys
array([0 1 2])
>>> idx
array([0 3 1 4 2 5])
>>> values
array([0 3 1 4 2 5])

>>> v2 =  -2 * ak.arange(10)
>>> v2
array([0 -2 -4 -6 -8 -10 -12 -14 -16 -18])
>>> _, idx2 = g.head(v2, 2, return_indices=True)
>>> _, values2 = g.head(v2, 2, return_indices=False)
>>> idx2
array([0 3 1 4 2 5])
>>> values2
array([0 -6 -2 -8 -4 -10])

is_registered() → bool[source]¶

Return True if the object is contained in the registry

Returns:: Indicates if the object is contained in the registry
Return type:: bool
Raises:: RegistrationError – Raised if there’s a server-side error or a mismatch of registered components

Notes

Objects registered with the server are immune to deletion until they are unregistered.

max(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the maximum of each group’s values.

Parameters:

values (pdarray) – The values to group and find maxima
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_maxima (pdarray) – One maximum per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if max is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if max is not supported for the values dtype

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.max(b)
(array([2, 3, 4]), array([4, 4, 3]))

mean(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the mean of each group’s values.

Parameters:

values (pdarray) – The values to group and average
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_means (pdarray, float64) – One mean value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.mean(b)
(array([2, 3, 4]), array([2.6666666666666665, 2.7999999999999998, 3]))

median(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the median of each group’s values.

Parameters:

values (pdarray) – The values to group and find median
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_medians (pdarray, float64) – One median value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

Examples

>>> a = ak.randint(1,5,9)
>>> a
array([4 1 4 3 2 2 2 3 3])
>>> g = ak.GroupBy(a)
>>> g.keys
array([4 1 4 3 2 2 2 3 3])
>>> b = ak.linspace(-5,5,9)
>>> b
array([-5 -3.75 -2.5 -1.25 0 1.25 2.5 3.75 5])
>>> g.median(b)
(array([1 2 3 4]), array([-3.75 1.25 3.75 -3.75]))

min(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the minimum of each group’s values.

Parameters:

values (pdarray) – The values to group and find minima
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_minima (pdarray) – One minimum per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if min is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if min is not supported for the values dtype

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.min(b)
(array([2, 3, 4]), array([1, 1, 3]))

mode(values: groupable) → Tuple[groupable, groupable][source]¶

Most common value in each group. If a group is multi-modal, return the modal value that occurs first.

Parameters:

values ((list of) pdarray-like) – The values from which to take the mode of each group

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) pdarray-like) – The most common value of each group

most_common(values)[source]¶: (Deprecated) See GroupBy.mode().

nunique(values: groupable) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the number of unique values in each group.

Parameters:

values (pdarray, int64) – The values to group and find unique values

Returns:

unique_keys (groupable) – The unique keys, in grouped order
group_nunique (groupable) – Number of unique values per unique key in the GroupBy instance

Raises:

TypeError – Raised if the dtype(s) of values array(s) does/do not support the nunique method
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if nunique is not supported for the values dtype

Examples

>>> data = ak.array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4])
>>> data
array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4])
>>> labels = ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4])
>>> labels
ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4])
>>> g = ak.GroupBy(labels)
>>> g.keys
ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4])
>>> g.nunique(data)
array([1,2,3,4]), array([2, 2, 3, 1])
#    Group (1,1,1) has values [3,4,3] -> there are 2 unique values 3&4
#    Group (2,2,2) has values [1,1,4] -> 2 unique values 1&4
#    Group (3,3,3) has values [3,4,1] -> 3 unique values
#    Group (4) has values [4] -> 1 unique value

objType(*args, **kwargs)¶

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

prod(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the product of each group’s values.

Parameters:

values (pdarray) – The values to group and multiply
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_products (pdarray, float64) – One product per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if prod is not supported for the values dtype

Notes

The return dtype is always float64.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.prod(b)
(array([2, 3, 4]), array([12, 108.00000000000003, 8.9999999999999982]))

register(user_defined_name: str) → GroupBy[source]¶

Parameters:

user_defined_name (str) – user defined name the GroupBy is to be registered under, this will be the root name for underlying components

Returns:

Return type:

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the GroupBy with the user_defined_name

Notes

Objects registered with the server are immune to deletion until they are unregistered.

sample(values: groupable, n=None, frac=None, replace=False, weights=None, random_state=None, return_indices=False, permute_samples=False)[source]¶

Return a random sample from each group. You can either specify the number of elements or the fraction of elements to be sampled. random_state can be used for reproducibility

Parameters:

values ((list of) pdarray-like) – The values from which to sample, according to their group membership.
n (int, optional) – Number of items to return for each group. Cannot be used with frac and must be no larger than the smallest group unless replace is True. Default is one if frac is None.
frac (float, optional) – Fraction of items to return. Cannot be used with n.
replace (bool, default False) – Allow or disallow sampling of the value more than once.
weights (pdarray, optional) – Default None results in equal probability weighting. If passed a pdarray, then values must have the same length as the groupby keys and will be used as sampling probabilities after normalization within each group. Weights must be non-negative with at least one positive element within each group.
random_state (int or ak.random.Generator, optional) – If int, seed for random number generator. If ak.random.Generator, use as given.
return_indices (bool, default False) – if True, return the indices of the sampled values. Otherwise, return the sample values.
permute_samples (bool, default False) – if True, return permute the samples according to group Otherwise, keep samples in original order.

Returns:

if return_indices is True, return the indices of the sampled values. Otherwise, return the sample values.

Return type:

size() → Tuple[groupable, pdarray][source]¶

Count the number of elements in each group, i.e. the number of times each key appears. This counts the total number of rows (including NaN values).

Parameters:

none

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears

See also

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 2, 3, 1, 2, 4, 3, 4, 3, 4])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.size()
>>> keys
array([1, 2, 3, 4])
>>> counts
array([1, 2, 4, 3])

std(values: pdarray, skipna: bool = True, ddof: int_scalars = 1) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the standard deviation of each group’s values.

Parameters:

values (pdarray) – The values to group and find standard deviation
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_stds (pdarray, float64) – One std value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

The standard deviation is the square root of the average of the squared deviations from the mean, i.e., std = sqrt(mean((x - x.mean())**2)).

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.std(b)
(array([2 3 4]), array([1.5275252316519465 1.0954451150103321 0]))

sum(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and sum each group’s values.

Parameters:

values (pdarray) – The values to group and sum
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_sums (pdarray) – One sum per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The grouped sum of a boolean pdarray returns integers.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.sum(b)
(array([2, 3, 4]), array([8, 14, 6]))

tail(values: groupable_element_type, n: int = 5, return_indices: bool = True) → Tuple[groupable, groupable_element_type][source]¶

Return the last n values from each group.

Parameters:

values ((list of) pdarray-like) – The values from which to select, according to their group membership.
n (int, optional, default = 5) – Maximum number of items to return for each group. If the number of values in a group is less than n, all the values from that group will be returned.
return_indices (bool, default False) – If True, return the indices of the sampled values. Otherwise, return the selected values.

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The last n items of each group. If return_indices is True, the result are indices. O.W. the result are values.

Examples

>>> a = ak.arange(10) %3
>>> a
array([0 1 2 0 1 2 0 1 2 0])
>>> v = ak.arange(10)
>>> v
array([0 1 2 3 4 5 6 7 8 9])
>>> g = GroupBy(a)
>>> unique_keys, idx = g.tail(v, 2, return_indices=True)
>>> _, values = g.tail(v, 2, return_indices=False)
>>> unique_keys
array([0 1 2])
>>> idx
array([6 9 4 7 5 8])
>>> values
array([6 9 4 7 5 8])

>>> v2 =  -2 * ak.arange(10)
>>> v2
array([0 -2 -4 -6 -8 -10 -12 -14 -16 -18])
>>> _, idx2 = g.tail(v2, 2, return_indices=True)
>>> _, values2 = g.tail(v2, 2, return_indices=False)
>>> idx2
array([6 9 4 7 5 8])
>>> values2
array([-12 -18 -8 -14 -10 -16])

to_hdf(prefix_path, dataset='groupby', mode='truncate', file_type='distribute')[source]¶

Save the GroupBy to HDF5. The result is a collection of HDF5 files, one file per locale of the arkouda server, where each filename starts with prefix_path.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files will share
dataset (str) – Name prefix for saved data within the HDF5 file
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, add data as a new column to existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.

Returns:

None
GroupBy is not currently supported by Parquet

unique(values: groupable)[source]¶

Return the set of unique values in each group, as a SegArray.

Parameters:

values ((list of) pdarray-like) – The values to unique

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) SegArray) – The unique values of each group

Raises:

TypeError – Raised if values is or contains Strings or Categorical

unregister()[source]¶

Unregister this GroupBy object in the arkouda server which was previously registered using register() and/or attached to using attach()

Raises:: RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister

Notes

Objects registered with the server are immune to deletion until they are unregistered.

unregister_groupby_by_name(user_defined_name: str) → None[source]¶

Function to unregister GroupBy object by name which was registered with the arkouda server via register()

Parameters:

user_defined_name (str) – Name under which the GroupBy object was registered

Raises:

TypeError – if user_defined_name is not a string
RegistrationError – if there is an issue attempting to unregister any underlying components

update_hdf(prefix_path: str, dataset: str = 'groupby', repack: bool = True)[source]¶

var(values: pdarray, skipna: bool = True, ddof: int_scalars = 1) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the variance of each group’s values.

Parameters:

values (pdarray) – The values to group and find variance
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_vars (pdarray, float64) – One var value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

The variance is the average of the squared deviations from the mean, i.e., var = mean((x - x.mean())**2).

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.var(b)
(array([2 3 4]), array([2.333333333333333 1.2 0]))

class arkouda.GroupBy[source]¶

Group an array or list of arrays by value, usually in preparation for aggregating the within-group values of another array.

Parameters:

keys ((list of) pdarray, Strings, or Categorical) – The array to group by value, or if list, the column arrays to group by row
assume_sorted (bool) – If True, assume keys is already sorted (Default: False)

nkeys¶

The number of key arrays (columns)

Type:: int

The length of the input array(s), i.e. number of rows

Type:: int

permutation¶

The permutation that sorts the keys array(s) by value (row)

Type:: pdarray

unique_keys¶

The unique values of the keys array(s), in grouped order

Type:: (list of) pdarray, Strings, or Categorical

ngroups¶

The length of the unique_keys array(s), i.e. number of groups

Type:: int

segments¶

The start index of each group in the grouped array(s)

Type:: pdarray

logger¶

Used for all logging operations

Type:: ArkoudaLogger

dropna¶

If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.

Type:: bool (default=True)

Raises:: TypeError – Raised if keys is a pdarray with a dtype other than int64

Notes

Integral pdarrays, Strings, and Categoricals are natively supported, but float64 and bool arrays are not.

For a user-defined class to be groupable, it must inherit from pdarray and define or overload the grouping API:

a ._get_grouping_keys() method that returns a list of pdarrays that can be (co)argsorted.

(Optional) a .group() method that returns the permutation that groups the array

If the input is a single array with a .group() method defined, method 2 will be used; otherwise, method 1 will be used.

AND(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise AND of values in each segment.

Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise AND reduction on each group.

Parameters:

values (pdarray, int64) – The values to group and reduce with AND

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise AND of values in segments corresponding to keys

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

OR(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise OR of values in each segment.

Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise OR reduction on each group.

Parameters:

values (pdarray, int64) – The values to group and reduce with OR

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise OR of values in segments corresponding to keys

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

Reductions(*args, **kwargs)¶

frozenset() -> empty frozenset object frozenset(iterable) -> frozenset object

Build an immutable unordered collection of unique elements.

XOR(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise XOR of values in each segment.

Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise XOR reduction on each group.

Parameters:

values (pdarray, int64) – The values to group and reduce with XOR

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise XOR of values in segments corresponding to keys

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

aggregate(values: groupable, operator: str, skipna: bool = True, ddof: int_scalars = 1) → Tuple[groupable, groupable][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and apply a reduction to each group’s values.

Parameters:

values (pdarray) – The values to group and reduce
operator (str) – The name of the reduction operator to use
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std

Returns:

unique_keys (groupable) – The unique keys, in grouped order
aggregates (groupable) – One aggregate value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if the requested operator is not supported for the values dtype

Examples

>>> keys = ak.arange(0, 10)
>>> vals = ak.linspace(-1, 1, 10)
>>> g = ak.GroupBy(keys)
>>> g.aggregate(vals, 'sum')
(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777768,
-0.55555555555555536, -0.33333333333333348, -0.11111111111111116,
0.11111111111111116, 0.33333333333333348, 0.55555555555555536, 0.77777777777777768,
1]))
>>> g.aggregate(vals, 'min')
(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777779,
-0.55555555555555558, -0.33333333333333337, -0.11111111111111116, 0.11111111111111116,
0.33333333333333326, 0.55555555555555536, 0.77777777777777768, 1]))

all(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and perform an “and” reduction on each group.

Parameters:

values (pdarray, bool) – The values to group and reduce with “and”

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

any(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and perform an “or” reduction on each group.

Parameters:

values (pdarray, bool) – The values to group and reduce with “or”

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

argmax(values: pdarray) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first maximum of each group’s values.

Parameters:

values (pdarray) – The values to group and find argmax

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argmaxima (pdarray, int64) – One index per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.argmax(b)
(array([2, 3, 4]), array([9, 3, 2]))

argmin(values: pdarray) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first minimum of each group’s values.

Parameters:

values (pdarray) – The values to group and find argmin

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argminima (pdarray, int64) – One index per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if argmin is not supported for the values dtype

Notes

The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.argmin(b)
(array([2, 3, 4]), array([5, 4, 2]))

attach(user_defined_name: str) → GroupBy[source]¶

Function to return a GroupBy object attached to the registered name in the arkouda server which was registered using register()

Parameters:: user_defined_name (str) – user defined name which GroupBy object was registered under
Returns:: The GroupBy object created by re-attaching to the corresponding server components
Return type:: GroupBy
Raises:: RegistrationError – if user_defined_name is not registered

broadcast(values: pdarray | Strings, permute: bool = True) → pdarray | Strings[source]¶

Fill each group’s segment with a constant value.

Parameters:

values (pdarray, Strings) – The values to put in each group’s segment
permute (bool) – If True (default), permute broadcast values back to the ordering of the original array on which GroupBy was called. If False, the broadcast values are grouped by value.

Returns:

The broadcasted values

Return type:

Raises:

TypeError – Raised if value is not a pdarray object
ValueError – Raised if the values array does not have one value per segment

Notes

Examples

>>> a = ak.array([0, 1, 0, 1, 0])
>>> values = ak.array([3, 5])
>>> g = ak.GroupBy(a)
# By default, result is in original order
>>> g.broadcast(values)
array([3, 5, 3, 5, 3])
# With permute=False, result is in grouped order
>>> g.broadcast(values, permute=False)
array([3, 3, 3, 5, 5]
>>> a = ak.randint(1,5,10)
>>> a
array([3, 1, 4, 4, 4, 1, 3, 3, 2, 2])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.size()
>>> g.broadcast(counts > 2)
array([True False True True True False True True False False])
>>> g.broadcast(counts == 3)
array([True False True True True False True True False False])
>>> g.broadcast(counts < 4)
array([True True True True True True True True True True])

build_from_components(user_defined_name: str | None = None, **kwargs) → GroupBy[source]¶

function to build a new GroupBy object from component keys and permutation.

Parameters:

user_defined_name (str (Optional) Passing a name will init the new GroupBy) – and assign it the given name
kwargs (dict Dictionary of components required for rebuilding the GroupBy.) – Expected keys are “orig_keys”, “permutation”, “unique_keys”, and “segments”

Returns:

The GroupBy object created by using the given components

Return type:

count(values: pdarray) → Tuple[groupable, pdarray][source]¶

Count the number of elements in each group. NaN values will be excluded from the total.

Parameters:

values (pdarray) – The values to be count by group (excluding NaN values).

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears (excluding NaN values).

Examples

>>> a = ak.array([1, 0, -1, 1, 0, -1])
>>> a
array([1 0 -1 1 0 -1])
>>> b = ak.array([1, np.nan, -1, np.nan, np.nan, -1], dtype = "float64")
>>> b
array([1.00000000000000000 nan -1.00000000000000000 nan nan -1.00000000000000000])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.count(b)
>>> keys
array([-1 0 1])
>>> counts
array([2 0 1])

first(values: groupable_element_type) → Tuple[groupable, groupable_element_type][source]¶

First value in each group.

Parameters:

values (pdarray-like) – The values from which to take the first of each group

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The first value of each group

from_return_msg(rep_msg)[source]¶

head(values: groupable_element_type, n: int = 5, return_indices: bool = True) → Tuple[groupable, groupable_element_type][source]¶

Return the first n values from each group.

Parameters:

values ((list of) pdarray-like) – The values from which to select, according to their group membership.
n (int, optional, default = 5) – Maximum number of items to return for each group. If the number of values in a group is less than n, all the values from that group will be returned.
return_indices (bool, default False) – If True, return the indices of the sampled values. Otherwise, return the selected values.

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The first n items of each group. If return_indices is True, the result are indices. O.W. the result are values.

Examples

>>> a = ak.arange(10) %3
>>> a
array([0 1 2 0 1 2 0 1 2 0])
>>> v = ak.arange(10)
>>> v
array([0 1 2 3 4 5 6 7 8 9])
>>> g = GroupBy(a)
>>> unique_keys, idx = g.head(v, 2, return_indices=True)
>>> _, values = g.head(v, 2, return_indices=False)
>>> unique_keys
array([0 1 2])
>>> idx
array([0 3 1 4 2 5])
>>> values
array([0 3 1 4 2 5])

>>> v2 =  -2 * ak.arange(10)
>>> v2
array([0 -2 -4 -6 -8 -10 -12 -14 -16 -18])
>>> _, idx2 = g.head(v2, 2, return_indices=True)
>>> _, values2 = g.head(v2, 2, return_indices=False)
>>> idx2
array([0 3 1 4 2 5])
>>> values2
array([0 -6 -2 -8 -4 -10])

is_registered() → bool[source]¶

Return True if the object is contained in the registry

Returns:: Indicates if the object is contained in the registry
Return type:: bool
Raises:: RegistrationError – Raised if there’s a server-side error or a mismatch of registered components

Notes

Objects registered with the server are immune to deletion until they are unregistered.

max(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the maximum of each group’s values.

Parameters:

values (pdarray) – The values to group and find maxima
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_maxima (pdarray) – One maximum per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if max is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if max is not supported for the values dtype

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.max(b)
(array([2, 3, 4]), array([4, 4, 3]))

mean(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the mean of each group’s values.

Parameters:

values (pdarray) – The values to group and average
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_means (pdarray, float64) – One mean value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.mean(b)
(array([2, 3, 4]), array([2.6666666666666665, 2.7999999999999998, 3]))

median(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the median of each group’s values.

Parameters:

values (pdarray) – The values to group and find median
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_medians (pdarray, float64) – One median value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

Examples

>>> a = ak.randint(1,5,9)
>>> a
array([4 1 4 3 2 2 2 3 3])
>>> g = ak.GroupBy(a)
>>> g.keys
array([4 1 4 3 2 2 2 3 3])
>>> b = ak.linspace(-5,5,9)
>>> b
array([-5 -3.75 -2.5 -1.25 0 1.25 2.5 3.75 5])
>>> g.median(b)
(array([1 2 3 4]), array([-3.75 1.25 3.75 -3.75]))

min(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the minimum of each group’s values.

Parameters:

values (pdarray) – The values to group and find minima
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_minima (pdarray) – One minimum per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if min is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if min is not supported for the values dtype

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.min(b)
(array([2, 3, 4]), array([1, 1, 3]))

mode(values: groupable) → Tuple[groupable, groupable][source]¶

Most common value in each group. If a group is multi-modal, return the modal value that occurs first.

Parameters:

values ((list of) pdarray-like) – The values from which to take the mode of each group

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) pdarray-like) – The most common value of each group

most_common(values)[source]¶: (Deprecated) See GroupBy.mode().

nunique(values: groupable) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the number of unique values in each group.

Parameters:

values (pdarray, int64) – The values to group and find unique values

Returns:

unique_keys (groupable) – The unique keys, in grouped order
group_nunique (groupable) – Number of unique values per unique key in the GroupBy instance

Raises:

TypeError – Raised if the dtype(s) of values array(s) does/do not support the nunique method
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if nunique is not supported for the values dtype

Examples

>>> data = ak.array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4])
>>> data
array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4])
>>> labels = ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4])
>>> labels
ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4])
>>> g = ak.GroupBy(labels)
>>> g.keys
ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4])
>>> g.nunique(data)
array([1,2,3,4]), array([2, 2, 3, 1])
#    Group (1,1,1) has values [3,4,3] -> there are 2 unique values 3&4
#    Group (2,2,2) has values [1,1,4] -> 2 unique values 1&4
#    Group (3,3,3) has values [3,4,1] -> 3 unique values
#    Group (4) has values [4] -> 1 unique value

objType(*args, **kwargs)¶

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

prod(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the product of each group’s values.

Parameters:

values (pdarray) – The values to group and multiply
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_products (pdarray, float64) – One product per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if prod is not supported for the values dtype

Notes

The return dtype is always float64.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.prod(b)
(array([2, 3, 4]), array([12, 108.00000000000003, 8.9999999999999982]))

register(user_defined_name: str) → GroupBy[source]¶

Parameters:

user_defined_name (str) – user defined name the GroupBy is to be registered under, this will be the root name for underlying components

Returns:

Return type:

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the GroupBy with the user_defined_name

Notes

Objects registered with the server are immune to deletion until they are unregistered.

sample(values: groupable, n=None, frac=None, replace=False, weights=None, random_state=None, return_indices=False, permute_samples=False)[source]¶

Return a random sample from each group. You can either specify the number of elements or the fraction of elements to be sampled. random_state can be used for reproducibility

Parameters:

values ((list of) pdarray-like) – The values from which to sample, according to their group membership.
n (int, optional) – Number of items to return for each group. Cannot be used with frac and must be no larger than the smallest group unless replace is True. Default is one if frac is None.
frac (float, optional) – Fraction of items to return. Cannot be used with n.
replace (bool, default False) – Allow or disallow sampling of the value more than once.
weights (pdarray, optional) – Default None results in equal probability weighting. If passed a pdarray, then values must have the same length as the groupby keys and will be used as sampling probabilities after normalization within each group. Weights must be non-negative with at least one positive element within each group.
random_state (int or ak.random.Generator, optional) – If int, seed for random number generator. If ak.random.Generator, use as given.
return_indices (bool, default False) – if True, return the indices of the sampled values. Otherwise, return the sample values.
permute_samples (bool, default False) – if True, return permute the samples according to group Otherwise, keep samples in original order.

Returns:

if return_indices is True, return the indices of the sampled values. Otherwise, return the sample values.

Return type:

size() → Tuple[groupable, pdarray][source]¶

Count the number of elements in each group, i.e. the number of times each key appears. This counts the total number of rows (including NaN values).

Parameters:

none

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears

See also

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 2, 3, 1, 2, 4, 3, 4, 3, 4])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.size()
>>> keys
array([1, 2, 3, 4])
>>> counts
array([1, 2, 4, 3])

std(values: pdarray, skipna: bool = True, ddof: int_scalars = 1) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the standard deviation of each group’s values.

Parameters:

values (pdarray) – The values to group and find standard deviation
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_stds (pdarray, float64) – One std value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

The standard deviation is the square root of the average of the squared deviations from the mean, i.e., std = sqrt(mean((x - x.mean())**2)).

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.std(b)
(array([2 3 4]), array([1.5275252316519465 1.0954451150103321 0]))

sum(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and sum each group’s values.

Parameters:

values (pdarray) – The values to group and sum
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_sums (pdarray) – One sum per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The grouped sum of a boolean pdarray returns integers.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.sum(b)
(array([2, 3, 4]), array([8, 14, 6]))

tail(values: groupable_element_type, n: int = 5, return_indices: bool = True) → Tuple[groupable, groupable_element_type][source]¶

Return the last n values from each group.

Parameters:

values ((list of) pdarray-like) – The values from which to select, according to their group membership.
n (int, optional, default = 5) – Maximum number of items to return for each group. If the number of values in a group is less than n, all the values from that group will be returned.
return_indices (bool, default False) – If True, return the indices of the sampled values. Otherwise, return the selected values.

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The last n items of each group. If return_indices is True, the result are indices. O.W. the result are values.

Examples

>>> a = ak.arange(10) %3
>>> a
array([0 1 2 0 1 2 0 1 2 0])
>>> v = ak.arange(10)
>>> v
array([0 1 2 3 4 5 6 7 8 9])
>>> g = GroupBy(a)
>>> unique_keys, idx = g.tail(v, 2, return_indices=True)
>>> _, values = g.tail(v, 2, return_indices=False)
>>> unique_keys
array([0 1 2])
>>> idx
array([6 9 4 7 5 8])
>>> values
array([6 9 4 7 5 8])

>>> v2 =  -2 * ak.arange(10)
>>> v2
array([0 -2 -4 -6 -8 -10 -12 -14 -16 -18])
>>> _, idx2 = g.tail(v2, 2, return_indices=True)
>>> _, values2 = g.tail(v2, 2, return_indices=False)
>>> idx2
array([6 9 4 7 5 8])
>>> values2
array([-12 -18 -8 -14 -10 -16])

to_hdf(prefix_path, dataset='groupby', mode='truncate', file_type='distribute')[source]¶

Save the GroupBy to HDF5. The result is a collection of HDF5 files, one file per locale of the arkouda server, where each filename starts with prefix_path.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files will share
dataset (str) – Name prefix for saved data within the HDF5 file
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, add data as a new column to existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.

Returns:

None
GroupBy is not currently supported by Parquet

unique(values: groupable)[source]¶

Return the set of unique values in each group, as a SegArray.

Parameters:

values ((list of) pdarray-like) – The values to unique

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) SegArray) – The unique values of each group

Raises:

TypeError – Raised if values is or contains Strings or Categorical

unregister()[source]¶

Unregister this GroupBy object in the arkouda server which was previously registered using register() and/or attached to using attach()

Raises:: RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister

Notes

Objects registered with the server are immune to deletion until they are unregistered.

unregister_groupby_by_name(user_defined_name: str) → None[source]¶

Function to unregister GroupBy object by name which was registered with the arkouda server via register()

Parameters:

user_defined_name (str) – Name under which the GroupBy object was registered

Raises:

TypeError – if user_defined_name is not a string
RegistrationError – if there is an issue attempting to unregister any underlying components

update_hdf(prefix_path: str, dataset: str = 'groupby', repack: bool = True)[source]¶

var(values: pdarray, skipna: bool = True, ddof: int_scalars = 1) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the variance of each group’s values.

Parameters:

values (pdarray) – The values to group and find variance
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_vars (pdarray, float64) – One var value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

The variance is the average of the squared deviations from the mean, i.e., var = mean((x - x.mean())**2).

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.var(b)
(array([2 3 4]), array([2.333333333333333 1.2 0]))

class arkouda.GroupBy[source]¶

Group an array or list of arrays by value, usually in preparation for aggregating the within-group values of another array.

Parameters:

keys ((list of) pdarray, Strings, or Categorical) – The array to group by value, or if list, the column arrays to group by row
assume_sorted (bool) – If True, assume keys is already sorted (Default: False)

nkeys¶

The number of key arrays (columns)

Type:: int

The length of the input array(s), i.e. number of rows

Type:: int

permutation¶

The permutation that sorts the keys array(s) by value (row)

Type:: pdarray

unique_keys¶

The unique values of the keys array(s), in grouped order

Type:: (list of) pdarray, Strings, or Categorical

ngroups¶

The length of the unique_keys array(s), i.e. number of groups

Type:: int

segments¶

The start index of each group in the grouped array(s)

Type:: pdarray

logger¶

Used for all logging operations

Type:: ArkoudaLogger

dropna¶

If True, and the groupby keys contain NaN values, the NaN values together with the corresponding row will be dropped. Otherwise, the rows corresponding to NaN values will be kept.

Type:: bool (default=True)

Raises:: TypeError – Raised if keys is a pdarray with a dtype other than int64

Notes

Integral pdarrays, Strings, and Categoricals are natively supported, but float64 and bool arrays are not.

For a user-defined class to be groupable, it must inherit from pdarray and define or overload the grouping API:

a ._get_grouping_keys() method that returns a list of pdarrays that can be (co)argsorted.

(Optional) a .group() method that returns the permutation that groups the array

If the input is a single array with a .group() method defined, method 2 will be used; otherwise, method 1 will be used.

AND(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise AND of values in each segment.

Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise AND reduction on each group.

Parameters:

values (pdarray, int64) – The values to group and reduce with AND

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise AND of values in segments corresponding to keys

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

OR(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise OR of values in each segment.

Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise OR reduction on each group.

Parameters:

values (pdarray, int64) – The values to group and reduce with OR

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise OR of values in segments corresponding to keys

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

Reductions(*args, **kwargs)¶

frozenset() -> empty frozenset object frozenset(iterable) -> frozenset object

Build an immutable unordered collection of unique elements.

XOR(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Bitwise XOR of values in each segment.

Using the permutation stored in the GroupBy instance, group another array of values and perform a bitwise XOR reduction on each group.

Parameters:

values (pdarray, int64) – The values to group and reduce with XOR

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
result (pdarray, int64) – Bitwise XOR of values in segments corresponding to keys

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not int64
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

aggregate(values: groupable, operator: str, skipna: bool = True, ddof: int_scalars = 1) → Tuple[groupable, groupable][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and apply a reduction to each group’s values.

Parameters:

values (pdarray) – The values to group and reduce
operator (str) – The name of the reduction operator to use
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std

Returns:

unique_keys (groupable) – The unique keys, in grouped order
aggregates (groupable) – One aggregate value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if the requested operator is not supported for the values dtype

Examples

>>> keys = ak.arange(0, 10)
>>> vals = ak.linspace(-1, 1, 10)
>>> g = ak.GroupBy(keys)
>>> g.aggregate(vals, 'sum')
(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777768,
-0.55555555555555536, -0.33333333333333348, -0.11111111111111116,
0.11111111111111116, 0.33333333333333348, 0.55555555555555536, 0.77777777777777768,
1]))
>>> g.aggregate(vals, 'min')
(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), array([-1, -0.77777777777777779,
-0.55555555555555558, -0.33333333333333337, -0.11111111111111116, 0.11111111111111116,
0.33333333333333326, 0.55555555555555536, 0.77777777777777768, 1]))

all(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and perform an “and” reduction on each group.

Parameters:

values (pdarray, bool) – The values to group and reduce with “and”

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if all is not supported for the values dtype

any(values: pdarray) → Tuple[pdarray | List[pdarray | Strings], pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and perform an “or” reduction on each group.

Parameters:

values (pdarray, bool) – The values to group and reduce with “or”

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_any (pdarray, bool) – One bool per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray or if the pdarray dtype is not bool
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

argmax(values: pdarray) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first maximum of each group’s values.

Parameters:

values (pdarray) – The values to group and find argmax

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argmaxima (pdarray, int64) – One index per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.argmax(b)
(array([2, 3, 4]), array([9, 3, 2]))

argmin(values: pdarray) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the location of the first minimum of each group’s values.

Parameters:

values (pdarray) – The values to group and find argmin

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_argminima (pdarray, int64) – One index per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if argmax is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if argmin is not supported for the values dtype

Notes

The returned indices refer to the original values array as passed in, not the permutation applied by the GroupBy instance.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.argmin(b)
(array([2, 3, 4]), array([5, 4, 2]))

attach(user_defined_name: str) → GroupBy[source]¶

Function to return a GroupBy object attached to the registered name in the arkouda server which was registered using register()

Parameters:: user_defined_name (str) – user defined name which GroupBy object was registered under
Returns:: The GroupBy object created by re-attaching to the corresponding server components
Return type:: GroupBy
Raises:: RegistrationError – if user_defined_name is not registered

broadcast(values: pdarray | Strings, permute: bool = True) → pdarray | Strings[source]¶

Fill each group’s segment with a constant value.

Parameters:

values (pdarray, Strings) – The values to put in each group’s segment
permute (bool) – If True (default), permute broadcast values back to the ordering of the original array on which GroupBy was called. If False, the broadcast values are grouped by value.

Returns:

The broadcasted values

Return type:

Raises:

TypeError – Raised if value is not a pdarray object
ValueError – Raised if the values array does not have one value per segment

Notes

Examples

>>> a = ak.array([0, 1, 0, 1, 0])
>>> values = ak.array([3, 5])
>>> g = ak.GroupBy(a)
# By default, result is in original order
>>> g.broadcast(values)
array([3, 5, 3, 5, 3])
# With permute=False, result is in grouped order
>>> g.broadcast(values, permute=False)
array([3, 3, 3, 5, 5]
>>> a = ak.randint(1,5,10)
>>> a
array([3, 1, 4, 4, 4, 1, 3, 3, 2, 2])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.size()
>>> g.broadcast(counts > 2)
array([True False True True True False True True False False])
>>> g.broadcast(counts == 3)
array([True False True True True False True True False False])
>>> g.broadcast(counts < 4)
array([True True True True True True True True True True])

build_from_components(user_defined_name: str | None = None, **kwargs) → GroupBy[source]¶

function to build a new GroupBy object from component keys and permutation.

Parameters:

user_defined_name (str (Optional) Passing a name will init the new GroupBy) – and assign it the given name
kwargs (dict Dictionary of components required for rebuilding the GroupBy.) – Expected keys are “orig_keys”, “permutation”, “unique_keys”, and “segments”

Returns:

The GroupBy object created by using the given components

Return type:

count(values: pdarray) → Tuple[groupable, pdarray][source]¶

Count the number of elements in each group. NaN values will be excluded from the total.

Parameters:

values (pdarray) – The values to be count by group (excluding NaN values).

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears (excluding NaN values).

Examples

>>> a = ak.array([1, 0, -1, 1, 0, -1])
>>> a
array([1 0 -1 1 0 -1])
>>> b = ak.array([1, np.nan, -1, np.nan, np.nan, -1], dtype = "float64")
>>> b
array([1.00000000000000000 nan -1.00000000000000000 nan nan -1.00000000000000000])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.count(b)
>>> keys
array([-1 0 1])
>>> counts
array([2 0 1])

first(values: groupable_element_type) → Tuple[groupable, groupable_element_type][source]¶

First value in each group.

Parameters:

values (pdarray-like) – The values from which to take the first of each group

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The first value of each group

from_return_msg(rep_msg)[source]¶

head(values: groupable_element_type, n: int = 5, return_indices: bool = True) → Tuple[groupable, groupable_element_type][source]¶

Return the first n values from each group.

Parameters:

values ((list of) pdarray-like) – The values from which to select, according to their group membership.
n (int, optional, default = 5) – Maximum number of items to return for each group. If the number of values in a group is less than n, all the values from that group will be returned.
return_indices (bool, default False) – If True, return the indices of the sampled values. Otherwise, return the selected values.

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The first n items of each group. If return_indices is True, the result are indices. O.W. the result are values.

Examples

>>> a = ak.arange(10) %3
>>> a
array([0 1 2 0 1 2 0 1 2 0])
>>> v = ak.arange(10)
>>> v
array([0 1 2 3 4 5 6 7 8 9])
>>> g = GroupBy(a)
>>> unique_keys, idx = g.head(v, 2, return_indices=True)
>>> _, values = g.head(v, 2, return_indices=False)
>>> unique_keys
array([0 1 2])
>>> idx
array([0 3 1 4 2 5])
>>> values
array([0 3 1 4 2 5])

>>> v2 =  -2 * ak.arange(10)
>>> v2
array([0 -2 -4 -6 -8 -10 -12 -14 -16 -18])
>>> _, idx2 = g.head(v2, 2, return_indices=True)
>>> _, values2 = g.head(v2, 2, return_indices=False)
>>> idx2
array([0 3 1 4 2 5])
>>> values2
array([0 -6 -2 -8 -4 -10])

is_registered() → bool[source]¶

Return True if the object is contained in the registry

Returns:: Indicates if the object is contained in the registry
Return type:: bool
Raises:: RegistrationError – Raised if there’s a server-side error or a mismatch of registered components

Notes

Objects registered with the server are immune to deletion until they are unregistered.

max(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the maximum of each group’s values.

Parameters:

values (pdarray) – The values to group and find maxima
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_maxima (pdarray) – One maximum per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if max is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if max is not supported for the values dtype

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.max(b)
(array([2, 3, 4]), array([4, 4, 3]))

mean(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the mean of each group’s values.

Parameters:

values (pdarray) – The values to group and average
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_means (pdarray, float64) – One mean value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.mean(b)
(array([2, 3, 4]), array([2.6666666666666665, 2.7999999999999998, 3]))

median(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the median of each group’s values.

Parameters:

values (pdarray) – The values to group and find median
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_medians (pdarray, float64) – One median value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

Examples

>>> a = ak.randint(1,5,9)
>>> a
array([4 1 4 3 2 2 2 3 3])
>>> g = ak.GroupBy(a)
>>> g.keys
array([4 1 4 3 2 2 2 3 3])
>>> b = ak.linspace(-5,5,9)
>>> b
array([-5 -3.75 -2.5 -1.25 0 1.25 2.5 3.75 5])
>>> g.median(b)
(array([1 2 3 4]), array([-3.75 1.25 3.75 -3.75]))

min(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the minimum of each group’s values.

Parameters:

values (pdarray) – The values to group and find minima
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_minima (pdarray) – One minimum per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object or if min is not supported for the values dtype
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if min is not supported for the values dtype

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.min(b)
(array([2, 3, 4]), array([1, 1, 3]))

mode(values: groupable) → Tuple[groupable, groupable][source]¶

Most common value in each group. If a group is multi-modal, return the modal value that occurs first.

Parameters:

values ((list of) pdarray-like) – The values from which to take the mode of each group

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) pdarray-like) – The most common value of each group

most_common(values)[source]¶: (Deprecated) See GroupBy.mode().

nunique(values: groupable) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and return the number of unique values in each group.

Parameters:

values (pdarray, int64) – The values to group and find unique values

Returns:

unique_keys (groupable) – The unique keys, in grouped order
group_nunique (groupable) – Number of unique values per unique key in the GroupBy instance

Raises:

TypeError – Raised if the dtype(s) of values array(s) does/do not support the nunique method
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if nunique is not supported for the values dtype

Examples

>>> data = ak.array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4])
>>> data
array([3, 4, 3, 1, 1, 4, 3, 4, 1, 4])
>>> labels = ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4])
>>> labels
ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4])
>>> g = ak.GroupBy(labels)
>>> g.keys
ak.array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4])
>>> g.nunique(data)
array([1,2,3,4]), array([2, 2, 3, 1])
#    Group (1,1,1) has values [3,4,3] -> there are 2 unique values 3&4
#    Group (2,2,2) has values [1,1,4] -> 2 unique values 1&4
#    Group (3,3,3) has values [3,4,1] -> 3 unique values
#    Group (4) has values [4] -> 1 unique value

objType(*args, **kwargs)¶

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

prod(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the product of each group’s values.

Parameters:

values (pdarray) – The values to group and multiply
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_products (pdarray, float64) – One product per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array
RuntimeError – Raised if prod is not supported for the values dtype

Notes

The return dtype is always float64.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.prod(b)
(array([2, 3, 4]), array([12, 108.00000000000003, 8.9999999999999982]))

register(user_defined_name: str) → GroupBy[source]¶

Parameters:

user_defined_name (str) – user defined name the GroupBy is to be registered under, this will be the root name for underlying components

Returns:

Return type:

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the GroupBy with the user_defined_name

Notes

Objects registered with the server are immune to deletion until they are unregistered.

sample(values: groupable, n=None, frac=None, replace=False, weights=None, random_state=None, return_indices=False, permute_samples=False)[source]¶

Return a random sample from each group. You can either specify the number of elements or the fraction of elements to be sampled. random_state can be used for reproducibility

Parameters:

values ((list of) pdarray-like) – The values from which to sample, according to their group membership.
n (int, optional) – Number of items to return for each group. Cannot be used with frac and must be no larger than the smallest group unless replace is True. Default is one if frac is None.
frac (float, optional) – Fraction of items to return. Cannot be used with n.
replace (bool, default False) – Allow or disallow sampling of the value more than once.
weights (pdarray, optional) – Default None results in equal probability weighting. If passed a pdarray, then values must have the same length as the groupby keys and will be used as sampling probabilities after normalization within each group. Weights must be non-negative with at least one positive element within each group.
random_state (int or ak.random.Generator, optional) – If int, seed for random number generator. If ak.random.Generator, use as given.
return_indices (bool, default False) – if True, return the indices of the sampled values. Otherwise, return the sample values.
permute_samples (bool, default False) – if True, return permute the samples according to group Otherwise, keep samples in original order.

Returns:

if return_indices is True, return the indices of the sampled values. Otherwise, return the sample values.

Return type:

size() → Tuple[groupable, pdarray][source]¶

Count the number of elements in each group, i.e. the number of times each key appears. This counts the total number of rows (including NaN values).

Parameters:

none

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
counts (pdarray, int64) – The number of times each unique key appears

See also

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 2, 3, 1, 2, 4, 3, 4, 3, 4])
>>> g = ak.GroupBy(a)
>>> keys,counts = g.size()
>>> keys
array([1, 2, 3, 4])
>>> counts
array([1, 2, 4, 3])

std(values: pdarray, skipna: bool = True, ddof: int_scalars = 1) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the standard deviation of each group’s values.

Parameters:

values (pdarray) – The values to group and find standard deviation
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_stds (pdarray, float64) – One std value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

The standard deviation is the square root of the average of the squared deviations from the mean, i.e., std = sqrt(mean((x - x.mean())**2)).

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.std(b)
(array([2 3 4]), array([1.5275252316519465 1.0954451150103321 0]))

sum(values: pdarray, skipna: bool = True) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and sum each group’s values.

Parameters:

values (pdarray) – The values to group and sum
skipna (bool) – boolean which determines if NANs should be skipped

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_sums (pdarray) – One sum per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The grouped sum of a boolean pdarray returns integers.

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.sum(b)
(array([2, 3, 4]), array([8, 14, 6]))

tail(values: groupable_element_type, n: int = 5, return_indices: bool = True) → Tuple[groupable, groupable_element_type][source]¶

Return the last n values from each group.

Parameters:

values ((list of) pdarray-like) – The values from which to select, according to their group membership.
n (int, optional, default = 5) – Maximum number of items to return for each group. If the number of values in a group is less than n, all the values from that group will be returned.
return_indices (bool, default False) – If True, return the indices of the sampled values. Otherwise, return the selected values.

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result (pdarray-like) – The last n items of each group. If return_indices is True, the result are indices. O.W. the result are values.

Examples

>>> a = ak.arange(10) %3
>>> a
array([0 1 2 0 1 2 0 1 2 0])
>>> v = ak.arange(10)
>>> v
array([0 1 2 3 4 5 6 7 8 9])
>>> g = GroupBy(a)
>>> unique_keys, idx = g.tail(v, 2, return_indices=True)
>>> _, values = g.tail(v, 2, return_indices=False)
>>> unique_keys
array([0 1 2])
>>> idx
array([6 9 4 7 5 8])
>>> values
array([6 9 4 7 5 8])

>>> v2 =  -2 * ak.arange(10)
>>> v2
array([0 -2 -4 -6 -8 -10 -12 -14 -16 -18])
>>> _, idx2 = g.tail(v2, 2, return_indices=True)
>>> _, values2 = g.tail(v2, 2, return_indices=False)
>>> idx2
array([6 9 4 7 5 8])
>>> values2
array([-12 -18 -8 -14 -10 -16])

to_hdf(prefix_path, dataset='groupby', mode='truncate', file_type='distribute')[source]¶

Save the GroupBy to HDF5. The result is a collection of HDF5 files, one file per locale of the arkouda server, where each filename starts with prefix_path.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files will share
dataset (str) – Name prefix for saved data within the HDF5 file
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, add data as a new column to existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.

Returns:

None
GroupBy is not currently supported by Parquet

unique(values: groupable)[source]¶

Return the set of unique values in each group, as a SegArray.

Parameters:

values ((list of) pdarray-like) – The values to unique

Returns:

unique_keys ((list of) pdarray-like) – The unique keys, in grouped order
result ((list of) SegArray) – The unique values of each group

Raises:

TypeError – Raised if values is or contains Strings or Categorical

unregister()[source]¶

Unregister this GroupBy object in the arkouda server which was previously registered using register() and/or attached to using attach()

Raises:: RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister

Notes

Objects registered with the server are immune to deletion until they are unregistered.

unregister_groupby_by_name(user_defined_name: str) → None[source]¶

Function to unregister GroupBy object by name which was registered with the arkouda server via register()

Parameters:

user_defined_name (str) – Name under which the GroupBy object was registered

Raises:

TypeError – if user_defined_name is not a string
RegistrationError – if there is an issue attempting to unregister any underlying components

update_hdf(prefix_path: str, dataset: str = 'groupby', repack: bool = True)[source]¶

var(values: pdarray, skipna: bool = True, ddof: int_scalars = 1) → Tuple[groupable, pdarray][source]¶

Using the permutation stored in the GroupBy instance, group another array of values and compute the variance of each group’s values.

Parameters:

values (pdarray) – The values to group and find variance
skipna (bool) – boolean which determines if NANs should be skipped
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var

Returns:

unique_keys ((list of) pdarray or Strings) – The unique keys, in grouped order
group_vars (pdarray, float64) – One var value per unique key in the GroupBy instance

Raises:

TypeError – Raised if the values array is not a pdarray object
ValueError – Raised if the key array size does not match the values size or if the operator is not in the GroupBy.Reductions array

Notes

The return dtype is always float64.

The variance is the average of the squared deviations from the mean, i.e., var = mean((x - x.mean())**2).

Examples

>>> a = ak.randint(1,5,10)
>>> a
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> g = ak.GroupBy(a)
>>> g.keys
array([3, 3, 4, 3, 3, 2, 3, 2, 4, 2])
>>> b = ak.randint(1,5,10)
>>> b
array([3, 3, 3, 4, 1, 1, 3, 3, 3, 4])
>>> g.var(b)
(array([2 3 4]), array([2.333333333333333 1.2 0]))

class arkouda.IPv4(values)[source]¶

Bases: arkouda.numpy.pdarrayclass.pdarray

Represent integers as IPv4 addresses.

Parameters:: values (pdarray, int64) – The integer IP addresses
Returns:: The same IP addresses
Return type:: IPv4

Notes

This class is a thin wrapper around pdarray that mostly affects how values are displayed to the user. Operators and methods will typically treat this class like an int64 pdarray.

export_uint()[source]¶

format(x)[source]¶: Format a single integer IP address as a string.

normalize(x)[source]¶: Take in an IP address as a string, integer, or IPAddress object, and convert it to an integer.

opeq(other, op)[source]¶

register(user_defined_name)[source]¶

Parameters:

user_defined_name (str) – user defined name the IPv4 is to be registered under, this will be the root name for underlying components

Returns:

The same IPv4 which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different IPv4s with the same name.

Return type:

IPv4

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the IPv4 with the user_defined_name

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

special_objType = 'IPv4'¶

to_hdf(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', file_type: str = 'distribute')[source]¶: Override of the pdarray to_hdf to store the special object type

to_list()[source]¶: Export array as a list of integers.

to_ndarray()[source]¶: Export array as a numpy array of integers.

update_hdf(prefix_path: str, dataset: str = 'array', repack: bool = True)[source]¶: Override the pdarray implementation so that the special object type will be used.

values¶

argsort(ascending=True)[source]¶

concat(other)[source]¶

equals(other: Index) → arkouda.numpy.dtypes.bool_scalars[source]¶

Whether Indexes are the same size, and all entries are equal.

Parameters:: other (object) – object to compare.
Returns:: True if the Indexes are the same, o.w. False.
Return type:: bool

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> i = ak.Index([1, 2, 3])
>>> i_cpy = ak.Index([1, 2, 3])
>>> i.equals(i_cpy)
True
>>> i2 = ak.Index([1, 2, 4])
>>> i.equals(i2)
False

MultiIndex case:

>>> arrays = [ak.array([1, 1, 2, 2]), ak.array(["red", "blue", "red", "blue"])]
>>> m = ak.MultiIndex(arrays, names=["numbers2", "colors2"])
>>> m.equals(m)
True
>>> arrays2 = [ak.array([1, 1, 2, 2]), ak.array(["red", "blue", "red", "green"])]
>>> m2 = ak.MultiIndex(arrays2, names=["numbers2", "colors2"])
>>> m.equals(m2)
False

static factory(index)[source]¶

classmethod from_return_msg(rep_msg)[source]¶

property index¶: This is maintained to support older code

property inferred_type: str¶: Return a string of the type inferred from the values.

is_registered()[source]¶

Return True iff the object is contained in the registry or is a component of a registered object.

Returns:: Indicates if the object is contained in the registry
Return type:: numpy.bool
Raises:: RegistrationError – Raised if there’s a server-side error or a mis-match of registered components

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

property is_unique¶: Property indicating if all values in the index are unique :rtype: bool - True if all values are unique, False otherwise.

lookup(key)[source]¶

map(arg: dict | arkouda.series.Series) → Index[source]¶

Map values of Index according to an input mapping.

Parameters:: arg (dict or Series) – The mapping correspondence.
Returns:: A new index with the values transformed by the mapping correspondence.
Return type:: arkouda.index.Index
Raises:: TypeError – Raised if arg is not of type dict or arkouda.Series. Raised if index values not of type pdarray, Categorical, or Strings.

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> idx = ak.Index(ak.array([2, 3, 2, 3, 4]))
>>> display(idx)
Index(array([2 3 2 3 4]), dtype='int64')
>>> idx.map({4: 25.0, 2: 30.0, 1: 7.0, 3: 5.0})
Index(array([30.00000000000000000 5.00000000000000000 30.00000000000000000
5.00000000000000000 25.00000000000000000]), dtype='float64')
>>> s2 = ak.Series(ak.array(["a","b","c","d"]), index = ak.array([4,2,1,3]))
>>> idx.map(s2)
Index(array(['b', 'b', 'd', 'd', 'a']), dtype='<U0')

max_list_size = 1000¶

memory_usage(unit='B')[source]¶

Return the memory usage of the Index values.

Parameters:: unit (str, default = "B") – Unit to return. One of {‘B’, ‘KB’, ‘MB’, ‘GB’}.
Returns:: Bytes of memory consumed.
Return type:: int

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> idx = Index(ak.array([1, 2, 3]))
>>> idx.memory_usage()
24

property names¶: Return Index or MultiIndex names.

property ndim¶: Number of dimensions of the underlying data, by definition 1.

See also

MultiIndex.ndim

property nlevels¶: Integer number of levels in this Index. An Index will always have 1 level. .. seealso:: MultiIndex.nlevels

objType = 'Index'¶

Sequence used for indexing and alignment.

The basic object storing axis labels for all DataFrame objects.

Parameters:

values (List, pdarray, Strings, Categorical, pandas.Categorical, pandas.Index, or Index)
name (str, default=None) – Name to be stored in the index.
False (allow_list =) – If False, list values will be converted to a pdarray. If True, list values will remain as a list, provided the data length is less than max_list_size.

:paramIf False, list values will be converted to a pdarray.: If True, list values will remain as a list, provided the data length is less than max_list_size.

Parameters:: 1000 (max_list_size =) – This is the maximum allowed data length for the values to be stored as a list object.
Raises:: ValueError – Raised if allow_list=True and the size of values is > max_list_size.

See also

MultiIndex

Examples

>>> ak.Index([1, 2, 3])
Index(array([1 2 3]), dtype='int64')

>>> ak.Index(list('abc'))
Index(array(['a', 'b', 'c']), dtype='<U0')

>>> ak.Index([1, 2, 3], allow_list=True)
Index([1, 2, 3], dtype='int64')

register(user_defined_name)[source]¶

Parameters:

user_defined_name (str) – user defined name the Index is to be registered under, this will be the root name for underlying components

Returns:

The same Index which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different Indexes with the same name.

Return type:

Index

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Index with the user_defined_name

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

registered_name: str | None = None¶

save(prefix_path: str, dataset: str = 'index', mode: str = 'truncate', compression: str | None = None, file_format: str = 'HDF5', file_type: str = 'distribute') → str[source]¶

DEPRECATED Save the index to HDF5 or Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.

If ‘append’, attempt to create new dataset in existing files.

Parameters:

compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
file_format (str {'HDF5', 'Parquet'}) – By default, saved files will be written to the HDF5 file format. If ‘Parquet’, the files will be written to the Parquet file format. This is case insensitive.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.

Return type:

string message indicating result of save operation

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray
ValueError – Raised if there is an error in parsing the prefix path pointing to file write location or if the mode parameter is neither truncate nor append
TypeError – Raised if any one of the prefix_path, dataset, or mode parameters is not a string. Raised if the Index values are a list.

Notes

The prefix_path must be visible to the arkouda server and the user must have write permission. Output files have names of the form <prefix_path>_LOCALE, where  ranges from 0 to numLocales. If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, a RuntimeError will result. Previously all files saved in Parquet format were saved with a .parquet file extension. This will require you to use load as if you saved the file with the extension. Try this if an older file is not being found. Any file extension can be used. The file I/O does not rely on the extension to determine the file format.

set_dtype(dtype)[source]¶

Change the data type of the index

Currently only aku.ip_address and ak.array are supported.

property shape¶

to_csv(prefix_path: str, dataset: str = 'index', col_delim: str = ',', overwrite: bool = False)[source]¶

Write Index to CSV file(s). File will contain a single column with the pdarray data. All CSV Files written by Arkouda include a header denoting data types of the columns.

prefix_path: str
The filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.

dataset: str
Column name to save the pdarray under. Defaults to “array”.

col_delim: str
Defaults to “,”. Value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.

overwrite: bool
Defaults to False. If True, any existing files matching your provided prefix_path will be overwritten. If False, an error will be returned if existing files are found.

str reponse message

ValueError
Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist.

RuntimeError
Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.

TypeError
Raised if we receive an unknown arkouda_type returned from the server. Raised if the Index values are a list.

CSV format is not currently supported by load/load_all operations

The column delimiter is expected to be the same for column names and data

Be sure that column delimiters are not found within your data.

All CSV files must delimit rows using newline (`

`) at this time.

to_dict(label)[source]¶

to_hdf(prefix_path: str, dataset: str = 'index', mode: str = 'truncate', file_type: str = 'distribute') → str[source]¶

Save the Index to HDF5. The object can be saved to a collection of files or single file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.

If ‘append’, attempt to create new dataset in existing files.

Parameters:

file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.

Return type:

string message indicating result of save operation

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray
TypeError – Raised if the Index values are a list.

Notes

The prefix_path must be visible to the arkouda server and the user must

have write permission. - Output files have names of the form <prefix_path>_LOCALE, where  ranges from 0 to numLocales for file_type=’distribute’. Otherwise, the file name will be prefix_path. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, a RuntimeError will result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.

to_list()[source]¶

to_ndarray()[source]¶

to_pandas()[source]¶: Return the equivalent Pandas Index.

to_parquet(prefix_path: str, dataset: str = 'index', mode: str = 'truncate', compression: str | None = None)[source]¶

Save the Index to Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.

If ‘append’, attempt to create new dataset in existing files.

Parameters:

Return type:

string message indicating result of save operation

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray
TypeError – Raised if the Index values are a list.

Notes

The prefix_path must be visible to the arkouda server and the user must

unregister()[source]¶

Unregister this Index object in the arkouda server which was previously registered using register() and/or attached to using attach()

Raises:: RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

update_hdf(prefix_path: str, dataset: str = 'index', repack: bool = True)[source]¶

Overwrite the dataset with the name provided with this Index object. If the dataset does not exist it is added.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.

Return type:

str - success message if successful

Raises:

RuntimeError – Raised if a server-side error is thrown saving the index

Notes

If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added
Because HDF5 deletes do not release memory, this will create a copy of the file with the new data

class arkouda.Iterable(origin, *, inst=True, name=None)¶

Bases: _BaseGenericAlias

A generic version of collections.abc.Iterable.

copy_with(params)¶

arkouda.LEN_SUFFIX = '_lengths'¶

class arkouda.LogLevel[source]¶

Bases: enum.Enum

Generic enumeration.

Derive from this class to define new enumerations.

CRITICAL = 'CRITICAL'¶

DEBUG = 'DEBUG'¶

ERROR = 'ERROR'¶

INFO = 'INFO'¶

WARN = 'WARN'¶

Bases: Index

argsort(ascending=True)[source]¶

concat(other)[source]¶

property dtype: numpy.dtype¶: Return the dtype object of the underlying data.

equal_levels(other: MultiIndex) → bool[source]¶: Return True if the levels of both MultiIndex objects are the same

get_level_values(level: str | int)[source]¶

property index¶: This is maintained to support older code

property inferred_type: str¶: Return a string of the type inferred from the values.

is_registered()[source]¶

Return True iff the object is contained in the registry or is a component of a registered object.

Returns:: Indicates if the object is contained in the registry
Return type:: numpy.bool
Raises:: RegistrationError – Raised if there’s a server-side error or a mis-match of registered components

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

levels: list¶

lookup(key)[source]¶

memory_usage(unit='B')[source]¶

Return the memory usage of the MultiIndex levels.

Parameters:: unit (str, default = "B") – Unit to return. One of {‘B’, ‘KB’, ‘MB’, ‘GB’}.
Returns:: Bytes of memory consumed.
Return type:: int

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> m = ak.index.MultiIndex([ak.array([1,2,3]),ak.array([4,5,6])])
>>> m.memory_usage()
48

property name¶: Return Index or MultiIndex name.

property names¶: Return Index or MultiIndex names.

property ndim¶: Number of dimensions of the underlying data, by definition 1.

See also

Index.ndim

property nlevels: int¶: Integer number of levels in this MultiIndex.

See also

Index.nlevels

objType = 'MultiIndex'¶

Sequence used for indexing and alignment.

The basic object storing axis labels for all DataFrame objects.

Parameters:

values (List, pdarray, Strings, Categorical, pandas.Categorical, pandas.Index, or Index)
name (str, default=None) – Name to be stored in the index.
False (allow_list =) – If False, list values will be converted to a pdarray. If True, list values will remain as a list, provided the data length is less than max_list_size.

:paramIf False, list values will be converted to a pdarray.: If True, list values will remain as a list, provided the data length is less than max_list_size.

Parameters:: 1000 (max_list_size =) – This is the maximum allowed data length for the values to be stored as a list object.
Raises:: ValueError – Raised if allow_list=True and the size of values is > max_list_size.

See also

MultiIndex

Examples

>>> ak.Index([1, 2, 3])
Index(array([1 2 3]), dtype='int64')

>>> ak.Index(list('abc'))
Index(array(['a', 'b', 'c']), dtype='<U0')

>>> ak.Index([1, 2, 3], allow_list=True)
Index([1, 2, 3], dtype='int64')

register(user_defined_name)[source]¶

Parameters:

user_defined_name (str) – user defined name the Index is to be registered under, this will be the root name for underlying components

Returns:

Return type:

MultiIndex

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Index with the user_defined_name

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

registered_name: str | None = None¶

set_dtype(dtype)[source]¶

Change the data type of the index

Currently only aku.ip_address and ak.array are supported.

to_dict(labels=None)[source]¶

to_hdf(prefix_path: str, dataset: str = 'index', mode: str = 'truncate', file_type: str = 'distribute') → str[source]¶

If ‘append’, attempt to create new dataset in existing files.

Parameters:: file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
Return type:: string message indicating result of save operation
Raises:: RuntimeError – Raised if a server-side error is thrown saving the pdarray.

Notes

The prefix_path must be visible to the arkouda server and the user must

to_list()[source]¶

to_ndarray()[source]¶

to_pandas()[source]¶: Return the equivalent Pandas Index.

unregister()[source]¶

Unregister this Index object in the arkouda server which was previously registered using register() and/or attached to using attach()

Raises:: RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

update_hdf(prefix_path: str, dataset: str = 'index', repack: bool = True)[source]¶

Overwrite the dataset with the name provided with this Index object. If the dataset does not exist it is added.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.

Return type:

str - success message if successful

Raises:

RuntimeError – Raised if a server-side error is thrown saving the index
TypeError – Raised if the Index levels are a list.

Notes

If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added
Because HDF5 deletes do not release memory, this will create a copy of the file with the new data

arkouda.NUMBER_FORMAT_STRINGS¶

arkouda.NumericDTypes¶

class arkouda.Optional[source]¶

Bases: _Final

Optional type.

Optional[X] is equivalent to Union[X, None].

class arkouda.Properties[source]¶

arkouda.RegisteredSymbols = '__RegisteredSymbols__'¶

exception arkouda.RegistrationError[source]¶

Bases: Exception

Error/Exception used when the Arkouda Server cannot register an object

exception arkouda.RegistrationError[source]¶

Bases: Exception

Error/Exception used when the Arkouda Server cannot register an object

exception arkouda.RegistrationError[source]¶

Bases: Exception

Error/Exception used when the Arkouda Server cannot register an object

class arkouda.Row(dict=None, /, **kwargs)[source]¶

Bases: collections.UserDict

This class is useful for printing and working with individual rows of a of an aku.DataFrame.

arkouda.SEG_SUFFIX = '_segments'¶

arkouda.ScalarDTypes¶

class arkouda.SegArray(segments, values, lengths=None, grouping=None)[source]¶

AND(x=None)[source]¶

OR(x=None)[source]¶

XOR(x=None)[source]¶

aggregate(op, x=None)[source]¶

all(x=None)[source]¶

any(x=None)[source]¶

append(other, axis=0)[source]¶

Append other to self, either vertically (axis=0, length of resulting SegArray increases), or horizontally (axis=1, each sub-array of other appends to the corresponding sub-array of self).

Parameters:

other (SegArray) – Array of sub-arrays to append
axis (0 or 1) – Whether to append vertically (0) or horizontally (1). If axis=1, other must be same size as self.

Returns:

axis=0: New SegArray containing all sub-arrays axis=1: New SegArray of same length, with pairs of sub-arrays concatenated

Return type:

append_single(x, prepend=False)[source]¶

Append a single value to each sub-array.

Parameters:: x (pdarray or scalar) – Single value to append to each sub-array
Returns:: Copy of original SegArray with values from x appended to each sub-array
Return type:: SegArray

argmax(x=None)[source]¶

argmin(x=None)[source]¶

classmethod attach(user_defined_name)[source]¶

Using the defined name, attach to a SegArray that has been registered to the Symbol Table

Parameters:: user_defined_name (str) – user defined name which the SegArray object was registered under
Returns:: The resulting SegArray
Return type:: SegArray
Raises:: RuntimeError – Raised if the server could not attach to the SegArray object

classmethod concat(x, axis=0, ordered=True)[source]¶

Concatenate a sequence of SegArrays

Parameters:

x (sequence of SegArray) – The SegArrays to concatenate
axis (0 or 1) – Select vertical (0) or horizontal (1) concatenation. If axis=1, all SegArrays must have same size.
ordered (bool) – Must be True. This option is present for compatibility only, because unordered concatenation is not yet supported.

Returns:

The input arrays joined into one SegArray

Return type:

copy()[source]¶: Return a deep copy.

dtype¶

filter(filter, discard_empty: bool = False)[source]¶

Filter values out of the SegArray object

Parameters:

filter (pdarray, list, or value) – The value/s to be filtered out of the SegArray
discard_empty (bool) – Defaults to False. When True, empty segments are removed from the return SegArray

Return type:

classmethod from_multi_array(m)[source]¶

Construct a SegArray from a list of columns. This essentially transposes the input, resulting in an array of rows.

Parameters:: m (list of pdarray or Strings) – List of columns, the rows of which will form the sub-arrays of the output
Returns:: Array of rows of input
Return type:: SegArray

classmethod from_parts(segments, values, lengths=None, grouping=None) → SegArray[source]¶

DEPRECATED Construct a SegArray object from its parts

Parameters:

segments (pdarray, int64) – Start index of each sub-array in the flattened values array
values (pdarray) – The flattened values of all sub-arrays
lengths (pdarray) – The length of each segment
grouping (GroupBy) – grouping of segments

Returns:

Data structure representing an array whose elements are variable-length arrays.

Return type:

pdarraysetops.intersect1d

Notes

Keyword args ‘lengths’ and ‘grouping’ are not user-facing. They are used by the attach method.

classmethod from_return_msg(rep_msg) → SegArray[source]¶

get_jth(j, return_origins=True, compressed=False, default=0)[source]¶

Select the j-th element of each sub-array, where possible.

Parameters:

j (int) – The index of the value to get from each sub-array. If j is negative, it counts backwards from the end of each sub-array.
return_origins (bool) – If True, return a logical index indicating where j is in bounds
compressed (bool) – If False, return array is same size as self, with default value where j is out of bounds. If True, the return array only contains values where j is in bounds.
default (scalar) – When compressed=False, the value to return when j is out of bounds for the sub-array

Returns:

val (pdarray) – compressed=False: The j-th value of each sub-array where j is in bounds and the default value where j is out of bounds. compressed=True: The j-th values of only the sub-arrays where j is in bounds
origin_indices (pdarray, bool) – A Boolean array that is True where j is in bounds for the sub-array.

Notes

If values are Strings, only the compressed format is supported.

get_length_n(n, return_origins=True)[source]¶

Return all sub-arrays of length n, as a list of columns.

Parameters:

n (int) – Length of sub-arrays to select
return_origins (bool) – Return a logical index indicating which sub-arrays are length n

Returns:

columns (list of pdarray) – An n-long list of pdarray, where each row is one of the n-long sub-arrays from the SegArray. The number of rows is the number of True values in the returned mask.
origin_indices (pdarray, bool) – Array of bool for each element of the SegArray, True where sub-array has length n.

get_ngrams(n, return_origins=True)[source]¶

Return all n-grams from all sub-arrays.

Parameters:

n (int) – Length of n-gram
return_origins (bool) – If True, return an int64 array indicating which sub-array each returned n-gram came from.

Returns:

ngrams (list of pdarray) – An n-long list of pdarrays, essentially a table where each row is an n-gram.
origin_indices (pdarray, int) – The index of the sub-array from which the corresponding n-gram originated

get_prefixes(n, return_origins=True, proper=True)[source]¶

Return all sub-array prefixes of length n (for sub-arrays that are at least n+1 long)

Parameters:

n (int) – Length of suffix
return_origins (bool) – If True, return a logical index indicating which sub-arrays were long enough to return an n-prefix
proper (bool) – If True, only return proper prefixes, i.e. from sub-arrays that are at least n+1 long. If False, allow the entire sub-array to be returned as a prefix.

Returns:

prefixes (list of pdarray) – An n-long list of pdarrays, essentially a table where each row is an n-prefix. The number of rows is the number of True values in the returned mask.
origin_indices (pdarray, bool) – Boolean array that is True where the sub-array was long enough to return an n-suffix, False otherwise.

get_suffixes(n, return_origins=True, proper=True)[source]¶

Return the n-long suffix of each sub-array, where possible

Parameters:

n (int) – Length of suffix
return_origins (bool) – If True, return a logical index indicating which sub-arrays were long enough to return an n-suffix
proper (bool) – If True, only return proper suffixes, i.e. from sub-arrays that are at least n+1 long. If False, allow the entire sub-array to be returned as a suffix.

Returns:

suffixes (list of pdarray) – An n-long list of pdarrays, essentially a table where each row is an n-suffix. The number of rows is the number of True values in the returned mask.
origin_indices (pdarray, bool) – Boolean array that is True where the sub-array was long enough to return an n-suffix, False otherwise.

property grouping¶

hash() → Tuple[arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.pdarrayclass.pdarray][source]¶

Compute a 128-bit hash of each segment.

Returns:: A tuple of two int64 pdarrays. The ith hash value is the concatenation of the ith values from each array.
Return type:: Tuple[pdarray,pdarray]

intersect(other)[source]¶

Computes the intersection of 2 SegArrays.

Parameters:: other (SegArray) – SegArray to compute against
Returns:: Segments are the 1d intersections of the segments of self and other
Return type:: SegArray

See also

Examples

>>> a = [1, 2, 3, 1, 4]
>>> b = [3, 1, 4, 5]
>>> c = [1, 3, 3, 5]
>>> d = [2, 2, 4]
>>> seg_a = ak.segarray(ak.array([0, len(a)]), ak.array(a+b))
>>> seg_b = ak.segarray(ak.array([0, len(c)]), ak.array(c+d))
>>> seg_a.intersect(seg_b)
SegArray([
[1, 3],
[4]
])

is_registered() → bool[source]¶

Checks if the name of the SegArray object is registered in the Symbol Table

Returns:: True if SegArray is registered, false if not
Return type:: bool

See also

register, unregister, attach

classmethod load(prefix_path, dataset='segarray', segment_name='segments', value_name='values')[source]¶

logger¶

max(x=None)[source]¶

mean(x=None)[source]¶

min(x=None)[source]¶

property nbytes¶

The size of the segarray in bytes.

Returns:: The size of the segarray in bytes.
Return type:: int

property non_empty¶

nunique(x=None)[source]¶

objType = 'SegArray'¶

prepend_single(x)[source]¶

prod(x=None)[source]¶

classmethod read_hdf(prefix_path, dataset='segarray')[source]¶

Load a saved SegArray from HDF5. All arguments must match what was supplied to SegArray.save()

Parameters:

prefix_path (str) – Directory and filename prefix
dataset (str) – Name prefix for saved data within the HDF5 files

Return type:

register(user_defined_name)[source]¶

Parameters:: user_defined_name (str) – user defined name which this SegArray object will be registered under
Returns:: The same SegArray which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different SegArrays with the same name.
Return type:: SegArray
Raises:: RegistrationError – Raised if the server could not register the SegArray object

Notes

Objects registered with the server are immune to deletion until they are unregistered.

See also

pdarraysetops.setdiff1d

registered_name: str | None = None¶

remove_repeats(return_multiplicity=False)[source]¶

Condense sequences of repeated values within a sub-array to a single value.

Parameters:

return_multiplicity (bool) – If True, also return the number of times each value was repeated.

Returns:

norepeats (SegArray) – Sub-arrays with runs of repeated values replaced with single value
multiplicity (SegArray) – If return_multiplicity=True, this array contains the number of times each value in the returned SegArray was repeated in the original SegArray.

save(prefix_path, dataset='segarray', mode='truncate', file_type='distribute')[source]¶

DEPRECATED Save the SegArray to HDF5. The object can be saved to a collection of files or single file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: By default, truncate (overwrite) output files, if they exist.

If ‘append’, attempt to create new dataset in existing files.

Parameters:: file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.
Return type:: string message indicating result of save operation
Raises:: RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

The prefix_path must be visible to the arkouda server and the user must

See also

to_hdf, load

segments¶

set_jth(i, j, v)[source]¶

Set the j-th element of each sub-array in a subset.

Parameters:

i (pdarray, int) – Indices of sub-arrays to set j-th element
j (int) – Index of value to set in each sub-array. If j is negative, it counts backwards from the end of the sub-array.
v (pdarray or scalar) – The value(s) to set. If v is a pdarray, it must have same length as i.

Raises:

ValueError – If j is out of bounds in any of the sub-arrays specified by i.

setdiff(other)[source]¶

Computes the set difference of 2 SegArrays.

Parameters:: other (SegArray) – SegArray to compute against
Returns:: Segments are the 1d set difference of the segments of self and other
Return type:: SegArray

See also

Examples

>>> a = [1, 2, 3, 1, 4]
>>> b = [3, 1, 4, 5]
>>> c = [1, 3, 3, 5]
>>> d = [2, 2, 4]
>>> seg_a = ak.segarray(ak.array([0, len(a)]), ak.array(a+b))
>>> seg_b = ak.segarray(ak.array([0, len(c)]), ak.array(c+d))
>>> seg_a.setdiff(seg_b)
SegArray([
[2, 4],
[1, 3, 5]
])

setxor(other)[source]¶

Computes the symmetric difference of 2 SegArrays.

Parameters:: other (SegArray) – SegArray to compute against
Returns:: Segments are the 1d symmetric difference of the segments of self and other
Return type:: SegArray

See also

pdarraysetops.setxor1d

Examples

>>> a = [1, 2, 3, 1, 4]
>>> b = [3, 1, 4, 5]
>>> c = [1, 3, 3, 5]
>>> d = [2, 2, 4]
>>> seg_a = ak.segarray(ak.array([0, len(a)]), ak.array(a+b))
>>> seg_b = ak.segarray(ak.array([0, len(c)]), ak.array(c+d))
>>> seg_a.setxor(seg_b)
SegArray([
[2, 4, 5],
[1, 3, 5, 2]
])

size¶

sum(x=None)[source]¶

to_hdf(prefix_path, dataset='segarray', mode='truncate', file_type='distribute')[source]¶

Save the SegArray to HDF5. The result is a collection of HDF5 files, one file per locale of the arkouda server, where each filename starts with prefix_path.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files will share
dataset (str) – Name prefix for saved data within the HDF5 file
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, add data as a new column to existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.

Return type:

None

See also

load

to_list()[source]¶

Convert the segarray into a list containing sub-arrays

Returns:: A list with the same sub-arrays (also list) as this segarray
Return type:: list

See also

Examples

>>> segarr = ak.SegArray(ak.array([0, 4, 7]), ak.arange(12))
>>> segarr.to_list()
[[0, 1, 2, 3], [4, 5, 6], [7, 8, 9, 10, 11]]
>>> type(segarr.to_list())
list

to_ndarray()[source]¶

Convert the array into a numpy.ndarray containing sub-arrays

Returns:: A numpy ndarray with the same sub-arrays (also numpy.ndarray) as this array
Return type:: np.ndarray

See also

Examples

>>> segarr = ak.SegArray(ak.array([0, 4, 7]), ak.arange(12))
>>> segarr.to_ndarray()
array([array([1, 2, 3, 4]), array([5, 6, 7]), array([8, 9, 10, 11, 12])])
>>> type(segarr.to_ndarray())
numpy.ndarray

to_parquet(prefix_path, dataset='segarray', mode: str = 'truncate', compression: str | None = None)[source]¶

Save the SegArray object to Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the object to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str :param mode: Deprecated.

Parameter kept to maintain functionality of other calls. Only Truncate supported. By default, truncate (overwrite) output files, if they exist. If ‘append’, attempt to create new dataset in existing files.

Parameters:

Return type:

string message indicating result of save operation

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray
ValueError – If write mode is not Truncate.

Notes

Append mode for Parquet has been deprecated. It was not implemented for SegArray.
The prefix_path must be visible to the arkouda server and the user must

have write permission. - Output files have names of the form <prefix_path>_LOCALE, where  ranges from 0 to numLocales for file_type=’distribute’. - If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, a RuntimeError will result. - Any file extension can be used.The file I/O does not rely on the extension to determine the file format.

transfer(hostname: str, port: arkouda.numpy.dtypes.int_scalars)[source]¶

Sends a Segmented Array to a different Arkouda server

Parameters:

hostname (str) – The hostname where the Arkouda server intended to receive the Segmented Array is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().

Return type:

A message indicating a complete transfer

Raises:

ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype

union(other)[source]¶

Computes the union of 2 SegArrays.

Parameters:: other (SegArray) – SegArray to compute against
Returns:: Segments are the 1d union of the segments of self and other
Return type:: SegArray

See also

pdarraysetops.union1d

Examples

>>> a = [1, 2, 3, 1, 4]
>>> b = [3, 1, 4, 5]
>>> c = [1, 3, 3, 5]
>>> d = [2, 2, 4]
>>> seg_a = ak.segarray(ak.array([0, len(a)]), ak.array(a+b))
>>> seg_b = ak.segarray(ak.array([0, len(c)]), ak.array(c+d))
>>> seg_a.union(seg_b)
SegArray([
[1, 2, 3, 4, 5],
[1, 2, 3, 4, 5]
])

unique(x=None)[source]¶

Return sub-arrays of unique values.

Parameters:: x (pdarray) – The values to unique, per group. By default, the values of this SegArray’s sub-arrays.
Returns:: Same number of sub-arrays as original SegArray, but elements in sub-array are unique and in sorted order.
Return type:: SegArray

unregister()[source]¶

Unregister this SegArray object in the arkouda server which was previously registered using register() and/or attached to using attach()

Return type:: None
Raises:: RuntimeError – Raised if the server could not unregister the SegArray object from the Symbol Table

Notes

Objects registered with the server are immune to deletion until they are unregistered.

See also

static unregister_segarray_by_name(user_defined_name)[source]¶

Using the defined name, remove the registered SegArray object from the Symbol Table

Parameters:: user_defined_name (str) – user defined name which the SegArray object was registered under
Return type:: None
Raises:: RuntimeError – Raised if the server could not unregister the SegArray object from the Symbol Table

update_hdf(prefix_path: str, dataset: str = 'segarray', repack: bool = True)[source]¶

Overwrite the dataset with the name provided with this SegArray object. If the dataset does not exist it is added.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.

Return type:

None

Raises:

RuntimeError – Raised if a server-side error is thrown saving the SegArray

Notes

If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added
Because HDF5 deletes do not release memory, this will create a copy of the file with the new data

valsize¶

values¶

class arkouda.Sequence(origin, *, inst=True, name=None)¶

Bases: _BaseGenericAlias

A generic version of collections.abc.Sequence.

copy_with(params)¶

class arkouda.Series[source]¶

One-dimensional arkouda array with axis labels.

Parameters:

index (pdarray, Strings) – an array of indices associated with the data array. If empty, it will default to a range of ints whose size match the size of the data. optional
data (Tuple, List, groupable_element_type, Series, SegArray) – a 1D array. Must not be None.

Raises:

TypeError – Raised if index is not a pdarray or Strings object Raised if data is not a pdarray, Strings, or Categorical object
ValueError – Raised if the index size does not match data size

Notes

The Series class accepts either positional arguments or keyword arguments. If entering positional arguments,

2 arguments entered:
argument 1 - data argument 2 - index

1 argument entered:
argument 1 - data

If entering 1 positional argument, it is assumed that this is the data argument. If only ‘data’ argument is passed in, Index will automatically be generated. If entering keywords,

‘data’ (see Parameters) ‘index’ (optional) must match size of ‘data’

add(b: Series) → Series[source]¶

argmax()¶

argmin()¶

property at¶

Accesses entries of a Series by label

Parameters:: key (pdarray, Strings, Series, list, supported_scalars) – The key or container of keys to access entries for

attach(label: str, nkeys: int = 1) → Series[source]¶

DEPRECATED Retrieve a series registered with arkouda

Parameters:

label (name used to register the series)
nkeys (number of keys, if a multi-index was registerd)

concat(arrays: List, axis: int = 0, index_labels: List[str] | None = None, value_labels: List[str] | None = None, ordered=False) → arkouda.dataframe.DataFrame | Series[source]¶

Concatenate in arkouda a list of arkouda Series or grouped arkouda arrays horizontally or

vertically. If a list of grouped arkouda arrays is passed they are converted to a series. Each grouping is a 2-tuple with the first item being the key(s) and the second being the value. If horizontal, each series or grouping must have the same length and the same index. The index of the series is converted to a column in the dataframe. If it is a multi-index,each level is converted to a column.

arrays: The list of series/groupings to concat. axis : Whether or not to do a verticle (axis=0) or horizontal (axis=1) concatenation index_labels: column names(s) to label the index. value_labels: column names to label values of each series. ordered: If True (default), the arrays will be appended in the order given. If False, array

data may be interleaved in blocks, which can greatly improve performance but results in non-deterministic ordering of elements.

axis=0: an arkouda series. axis=1: an arkouda dataframe.

diff() → Series[source]¶: Diffs consecutive values of the series.

Returns a new series with the same index and length. First value is set to NaN.

dt(series)¶

property dtype¶

fillna(value) → Series[source]¶

Fill NA/NaN values using the specified method.

Parameters:: value (scalar, Series, or pdarray) – Value to use to fill holes (e.g. 0), alternately a Series of values specifying which value to use for each index. Values not in the Series will not be filled. This value cannot be a list.
Returns:: Object with missing values filled.
Return type:: Series

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> from arkouda import Series

>>> data = ak.Series([1, np.nan, 3, np.nan, 5])
>>> data

	0
0	1
1	nan
2	3
3	nan
4	5

>>> fill_values1 = ak.ones(5)
>>> data.fillna(fill_values1)

	0
0	1
1	1
2	3
3	1
4	5

>>> fill_values2 = Series(ak.ones(5))
>>> data.fillna(fill_values2)

	0
0	1
1	1
2	3
3	1
4	5

>>> fill_values3 = 100.0
>>> data.fillna(fill_values3)

	0
0	1
1	100
2	3
3	100
4	5

from_return_msg(repMsg: str) → Series[source]¶

Return a Series instance pointing to components created by the arkouda server. The user should not call this function directly.

Parameters:

repMsg (str) –

delimited string containing the values and indexes

Returns:

A Series representing a set of pdarray components on the server

Return type:

Raises:

RuntimeError – Raised if a server-side error is thrown in the process of creating the Series instance

has_repeat_labels() → bool[source]¶: Returns whether the Series has any labels that appear more than once

hasnans() → bool_scalars[source]¶

Return True if there are any NaNs.

Return type:: bool

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> from arkouda import Series
>>> import numpy as np

>>> s = ak.Series(ak.array([1, 2, 3, np.nan]))
>>> s

>>> s.hasnans
True

head(n: int = 10) → Series[source]¶: Return the first n values of the series

property iat: Series¶

Accesses entries of a Series by position

Parameters:: key (int) – The positions or container of positions to access entries for

property iloc: Series¶

Accesses entries of a Series by position

Parameters:: key (int) – The positions or container of positions to access entries for

is_registered() → bool[source]¶

Return True iff the object is contained in the registry or is a component of a registered object.

Returns:: Indicates if the object is contained in the registry
Return type:: numpy.bool
Raises:: RegistrationError – Raised if there’s a server-side error or a mis-match of registered components

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

isin(lst: pdarray | Strings | List) → Series[source]¶: Find series elements whose values are in the specified list

Either a python list or an arkouda array.

Arkouda boolean which is true for elements that are in the list and false otherwise.

isna() → Series[source]¶

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values, such as numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings ‘’ are not considered NA values.

Returns:: Mask of bool values for each element in Series that indicates whether an element is an NA value.
Return type:: arkouda.series.Series

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> from arkouda import Series
>>> import numpy as np

>>> s = Series(ak.array([1, 2, np.nan]), index = ak.array([1, 2, 4]))
>>> s.isna()

	0
1	False
2	False
4	True

isnull() → Series[source]¶

Series.isnull is an alias for Series.isna.

Detect missing values.

Returns:: Mask of bool values for each element in Series that indicates whether an element is an NA value.
Return type:: arkouda.series.Series

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> from arkouda import Series
>>> import numpy as np

>>> s = Series(ak.array([1, 2, np.nan]), index = ak.array([1, 2, 4]))
>>> s.isnull()

	0
1	False
2	False
4	True

property loc: Series¶

Accesses entries of a Series by label

Parameters:: key (pdarray, Strings, Series, list, supported_scalars) – The key or container of keys to access entries for

locate(key: int | pdarray | Index | Series | List | Tuple) → Series[source]¶: Lookup values by index label

The input can be a scalar, a list of scalers, or a list of lists (if the series has a MultiIndex). As a special case, if a Series is used as the key, the series labels are preserved with its values use as the key.

Keys will be turned into arkouda arrays as needed.

A Series containing the values corresponding to the key.

map(arg: dict | Series) → Series[source]¶

Map values of Series according to an input mapping.

Parameters:: arg (dict or Series) – The mapping correspondence.
Returns:: A new series with the same index as the caller. When the input Series has Categorical values, the return Series will have Strings values. Otherwise, the return type will match the input type.
Return type:: arkouda.series.Series
Raises:: TypeError – Raised if arg is not of type dict or arkouda.Series. Raised if series values not of type pdarray, Categorical, or Strings.

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> s = ak.Series(ak.array([2, 3, 2, 3, 4]))
>>> display(s)

	0
0	2
1	3
2	2
3	3
4	4

>>> s.map({4: 25.0, 2: 30.0, 1: 7.0, 3: 5.0})

	0
0	30.0
1	5.0
2	30.0
3	5.0
4	25.0

>>> s2 = ak.Series(ak.array(["a","b","c","d"]), index = ak.array([4,2,1,3]))
>>> s.map(s2)

	0
0	b
1	b
2	d
3	d
4	a

max()¶

mean()¶

memory_usage(index: bool = True, unit='B') → int[source]¶

Return the memory usage of the Series.

The memory usage can optionally include the contribution of the index.

Parameters:

index (bool, default True) – Specifies whether to include the memory usage of the Series index.
unit (str, default = "B") – Unit to return. One of {‘B’, ‘KB’, ‘MB’, ‘GB’}.

Returns:

Bytes of memory consumed.

Return type:

int

Examples

>>> from arkouda.series import Series
>>> s = ak.Series(ak.arange(3))
>>> s.memory_usage()
48

Not including the index gives the size of the rest of the data, which is necessarily smaller:

>>> s.memory_usage(index=False)
24

Select the units:

>>> s = ak.Series(ak.arange(3000))
>>> s.memory_usage(unit="KB")
46.875

min()¶

property ndim¶

notna() → Series[source]¶

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings ‘’ are not considered NA values. NA values, such as numpy.NaN, get mapped to False values.

Returns:: Mask of bool values for each element in Series that indicates whether an element is not an NA value.
Return type:: arkouda.series.Series

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> from arkouda import Series
>>> import numpy as np

>>> s = Series(ak.array([1, 2, np.nan]), index = ak.array([1, 2, 4]))
>>> s.notna()

	0
1	True
2	True
4	False

notnull() → Series[source]¶

Series.notnull is an alias for Series.notna.

Detect existing (non-missing) values.

Returns:: Mask of bool values for each element in Series that indicates whether an element is not an NA value.
Return type:: arkouda.series.Series

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> from arkouda import Series
>>> import numpy as np

>>> s = Series(ak.array([1, 2, np.nan]), index = ak.array([1, 2, 4]))
>>> s.notnull()

	0
1	True
2	True
4	False

objType(*args, **kwargs)¶

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

pdconcat(arrays: List, axis: int = 0, labels: Strings | None = None) → pd.Series | pd.DataFrame[source]¶: Concatenate a list of arkouda Series or grouped arkouda arrays, returning a PANDAS object.

If a list of grouped arkouda arrays is passed they are converted to a series. Each grouping is a 2-tuple with the first item being the key(s) and the second being the value.

If horizontal, each series or grouping must have the same length and the same index. The index of the series is converted to a column in the dataframe. If it is a multi-index,each level is converted to a column.

arrays: The list of series/groupings to concat. axis : Whether or not to do a verticle (axis=0) or horizontal (axis=1) concatenation labels: names to give the columns of the data frame.

axis=0: a local PANDAS series axis=1: a local PANDAS dataframe

prod()¶

register(user_defined_name: str)[source]¶

Parameters:

user_defined_name (str) – user defined name the Series is to be registered under, this will be the root name for underlying components

Returns:

The same Series which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different Series with the same name.

Return type:

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Series with the user_defined_name

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

property shape¶

sort_index(ascending: bool = True) → Series[source]¶

Sort the series by its index

ascendingbool
Sort values in ascending (default) or descending order.

A new Series sorted.

sort_values(ascending: bool = True) → Series[source]¶

Sort the series numerically

ascendingbool
Sort values in ascending (default) or descending order.

A new Series sorted smallest to largest

std()¶

str_acc(series)¶

sum()¶

tail(n: int = 10) → Series[source]¶: Return the last n values of the series

to_dataframe(index_labels: List[str] | None = None, value_label: str | None = None) → arkouda.dataframe.DataFrame[source]¶: Converts series to an arkouda data frame

index_labels: column names(s) to label the index. value_label: column name to label values.

An arkouda dataframe.

to_list() → list[source]¶

to_markdown(mode='wt', index=True, tablefmt='grid', storage_options=None, **kwargs)[source]¶

Print Series in Markdown-friendly format.

Parameters:

mode (str, optional) – Mode in which file is opened, “wt” by default.
index (bool, optional, default True) – Add index (row) labels.
tablefmt (str = "grid") – Table format to call from tablulate: https://pypi.org/project/tabulate/
storage_options (dict, optional) – Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc., if using a URL that will be parsed by fsspec, e.g., starting “s3://”, “gcs://”. An error will be raised if providing this argument with a non-fsspec URL. See the fsspec and backend storage implementation docs for the set of allowed keys and values.
**kwargs – These parameters will be passed to tabulate.

Note

This function should only be called on small Series as it calls pandas.Series.to_markdown: https://pandas.pydata.org/docs/reference/api/pandas.Series.to_markdown.html

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> s = ak.Series(["elk", "pig", "dog", "quetzal"], name="animal")
>>> print(s.to_markdown())
|    | animal   |
|---:|:---------|
|  0 | elk      |
|  1 | pig      |
|  2 | dog      |
|  3 | quetzal  |

Output markdown with a tabulate option.

>>> print(s.to_markdown(tablefmt="grid"))
+----+----------+
|    | animal   |
+====+==========+
|  0 | elk      |
+----+----------+
|  1 | pig      |
+----+----------+
|  2 | dog      |
+----+----------+
|  3 | quetzal  |
+----+----------+

to_ndarray()[source]¶

to_pandas() → pd.Series[source]¶: Convert the series to a local PANDAS series

topn(n: int = 10) → Series[source]¶: Return the top values of the series

n: Number of values to return

A new Series with the top values

unregister()[source]¶

Unregister this Series object in the arkouda server which was previously registered using register() and/or attached to using attach()

Raises:: RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

Validates type requirements for keys when reading or writing the Series. Also converts list and tuple arguments into pdarrays.

Parameters:

key (Series, pdarray, Strings, Categorical, List, supported_scalars) – The key or container of keys that might be used to index into the Series.

Return type:

The validated key(s), with lists and tuples converted to pdarrays

Raises:

TypeError – Raised if keys are not boolean values or the type of the labels Raised if key is not one of the supported types
KeyError – Raised if container of keys has keys not present in the Series
IndexError – Raised if the length of a boolean key array is different from the Series

Validates type requirements for values being written into the Series. Also converts list and tuple arguments into pdarrays.

Parameters:

val (pdarray, Strings, list, supported_scalars) – The value or container of values that might be assigned into the Series.

Return type:

The validated value, with lists converted to pdarrays

Raises:

TypeError –

Raised if val is not the same type or a container with elements: of the same time as the Series

Raised if val is a string or Strings type. Raised if val is not one of the supported types

value_counts(sort: bool = True) → Series[source]¶: Return a Series containing counts of unique values.

The resulting object will be in descending order so that the first element is the most frequently-occurring element.

sort : Boolean. Whether or not to sort the results. Default is true.

var()¶

arkouda.SeriesDTypes¶

arkouda.SortingAlgorithm¶

class arkouda.StringAccessor(series)[source]¶

Bases: Properties

series¶

class arkouda.Strings[source]¶

Represents an array of strings whose data resides on the arkouda server. The user should not call this class directly; rather its instances are created by other arkouda functions.

entry¶

Encapsulation of a Segmented Strings array contained on the arkouda server. This is a composite of

offsets array: starting indices for each string

bytes array: raw bytes of all strings joined by nulls

Type:: pdarray

size¶

The number of strings in the array

Type:: int_scalars

nbytes¶

The total number of bytes in all strings

Type:: int_scalars

ndim¶

The rank of the array (currently only rank 1 arrays supported)

Type:: int_scalars

shape¶

The sizes of each dimension of the array

Type:: tuple

dtype¶

The dtype is ak.str

Type:: dtype

logger¶

Used for all logging operations

Type:: ArkoudaLogger

Notes

Strings is composed of two pdarrays: (1) offsets, which contains the starting indices for each string and (2) bytes, which contains the raw bytes of all strings, delimited by nulls.

BinOps(*args, **kwargs)¶

frozenset() -> empty frozenset object frozenset(iterable) -> frozenset object

Build an immutable unordered collection of unique elements.

astype(dtype: np.dtype | str) → pdarray[source]¶

Cast values of Strings object to provided dtype

Parameters:: dtype (np.dtype or str) – Dtype to cast to
Returns:: An arkouda pdarray with values converted to the specified data type
Return type:: ak.pdarray

Notes

This is essentially shorthand for ak.cast(x, ‘<dtype>’) where x is a pdarray.

attach(user_defined_name: str) → Strings[source]¶

class method to return a Strings object attached to the registered name in the arkouda server which was registered using register()

Parameters:: user_defined_name (str) – user defined name which the Strings object was registered under
Returns:: the Strings object registered with user_defined_name in the arkouda server
Return type:: Strings object
Raises:: TypeError – Raised if user_defined_name is not a str

See also

register, unregister

Notes

Registered names/Strings objects in the server are immune to deletion until they are unregistered.

cached_regex_patterns() → List[source]¶: Returns the regex patterns for which Match objects have been cached

capitalize() → Strings[source]¶

Returns a new Strings from the original replaced with the first letter capitilzed and the remaining letters lowercase.

Returns:: Strings from the original replaced with the capitalized equivalent.
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown.

See also

Strings.lower, String.upper, String.title

Examples

>>> strings = ak.array([f'StrINgS aRe Here {i}' for i in range(5)])
>>> strings
array(['StrINgS aRe Here 0', 'StrINgS aRe Here 1', 'StrINgS aRe Here 2', 'StrINgS aRe Here 3', 'StrINgS aRe Here 4'])
>>> strings.title()
array(['Strings Are Here 0', 'Strings Are Here 1', 'Strings Are Here 2', 'Strings Are Here 3', 'Strings Are Here 4'])

contains(substr: bytes | str_scalars, regex: bool = False) → pdarray[source]¶

Check whether each element contains the given substring.

Parameters:

substr (bytes or str_scalars) – The substring in the form of string or byte array to search for
regex (bool, default=False) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

True for elements that contain substr, False otherwise

Return type:

pdarray, bool

Raises:

TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

Examples

>>> strings = ak.array([f'{i} string {i}' for i in range(1, 6)])
>>> strings
array(['1 string 1', '2 string 2', '3 string 3', '4 string 4', '5 string 5'])
>>> strings.contains('string')
array([True True True True True])
>>> strings.contains('string \d', regex=True)
array([True True True True True])

decode(fromEncoding: str, toEncoding: str = 'UTF-8') → Strings[source]¶

Return a new strings object in fromEncoding, expecting that the current Strings is encoded in toEncoding

Parameters:

fromEncoding (str) – The current encoding of the strings object
toEncoding (str, default="UTF-8") – The encoding that the strings will be converted to, default to UTF-8

Returns:

A new Strings object in toEncoding

Return type:

Raises:

RuntimeError – Raised if there is a server-side error thrown

encode(toEncoding: str, fromEncoding: str = 'UTF-8') → Strings[source]¶

Return a new strings object in toEncoding, expecting that the current Strings is encoded in fromEncoding

Parameters:

toEncoding (str) – The encoding that the strings will be converted to
fromEncoding (str, default="UTF-8") – The current encoding of the strings object, default to UTF-8

Returns:

A new Strings object in toEncoding

Return type:

Strings.findall, Strings.match

Raises:

RuntimeError – Raised if there is a server-side error thrown

endswith(substr: bytes | str_scalars, regex: bool = False) → pdarray[source]¶

Check whether each element ends with the given substring.

Parameters:

substr (bytes or str_scalars) – The suffix to search for
regex (bool, default=False) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

True for elements that end with substr, False otherwise

Return type:

pdarray, bool

Raises:

TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

Examples

>>> strings_start = ak.array([f'{i} string' for i in range(1,6)])
>>> strings_start
array(['1 string', '2 string', '3 string', '4 string', '5 string'])
>>> strings_start.endswith('ing')
array([True True True True True])
>>> strings_end = ak.array([f'string {i}' for i in range(1, 6)])
>>> strings_end
array(['string 1', 'string 2', 'string 3', 'string 4', 'string 5'])
>>> strings_end.endswith('ing \d', regex = True)
array([True True True True True])

equals(other: Any) → bool_scalars[source]¶

Whether Strings are the same size and all entries are equal.

Parameters:: other (Any) – object to compare.
Returns:: True if the Strings are the same, o.w. False.
Return type:: bool

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> s = ak.array(["a", "b", "c"])
>>> s_cpy = ak.array(["a", "b", "c"])
>>> s.equals(s_cpy)
True
>>> s2 = ak.array(["a", "x", "c"])
>>> s.equals(s2)
False

find_locations(pattern: bytes | str_scalars) → Tuple[pdarray, pdarray, pdarray][source]¶

Finds pattern matches and returns pdarrays containing the number, start postitions, and lengths of matches

Parameters:

pattern (bytes or str_scalars) – The regex pattern used to find matches

Returns:

pdarray, int64 – For each original string, the number of pattern matches
pdarray, int64 – The start positons of pattern matches
pdarray, int64 – The lengths of pattern matches

Raises:

TypeError – Raised if the pattern parameter is not bytes or str_scalars
ValueError – Raised if pattern is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Examples

>>> strings = ak.array([f'{i} string {i}' for i in range(1, 6)])
>>> num_matches, starts, lens = strings.find_locations('\d')
>>> num_matches
array([2 2 2 2 2])
>>> starts
array([0 9 0 9 0 9 0 9 0 9])
>>> lens
array([1 1 1 1 1 1 1 1 1 1])

findall(pattern: bytes | str_scalars, return_match_origins: bool = False) → Strings | Tuple[source]¶

Return a new Strings containg all non-overlapping matches of pattern

Parameters:

pattern (bytes or str_scalars) – Regex used to find matches
return_match_origins (bool, default=False) – If True, return a pdarray containing the index of the original string each pattern match is from

Returns:

Strings – Strings object containing only pattern matches
pdarray, int64 (optional) – The index of the original string each pattern match is from

Raises:

TypeError – Raised if the pattern parameter is not bytes or str_scalars
ValueError – Raised if pattern is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Strings.find_locations

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.findall('_+', return_match_origins=True)
(array(['_', '___', '____', '__', '___', '____', '___']), array([0 0 1 3 3 3 3]))

flatten() → Strings[source]¶

Return a copy of the array collapsed into one dimension.

Return type:: A copy of the input array, flattened to one dimension.

Note

As multidimensional Strings are currently supported, flatten on a Strings object will always return itself.

from_parts(offset_attrib: pdarray | str, bytes_attrib: pdarray | str) → Strings[source]¶

Factory method for creating a Strings object from an Arkouda server response where the arrays are separate components.

Parameters:

offset_attrib (pdarray or str) – the array containing the offsets
bytes_attrib (pdarray or str) – the array containing the string values

Returns:

object representing a segmented strings array on the server

Return type:

Raises:

RuntimeError – Raised if there’s an error converting a server-returned str-descriptor

Notes

This factory method is used when we construct the parts of a Strings object on the client side and transfer the offsets & bytes separately to the server. This results in two entries in the symbol table and we need to instruct the server to assemble the into a composite entity.

from_return_msg(rep_msg: str) → Strings[source]¶

Factory method for creating a Strings object from an Arkouda server response message

Parameters:: rep_msg (str) – Server response message currently of form created name type size ndim shape itemsize+created bytes.size 1234
Returns:: object representing a segmented strings array on the server
Return type:: Strings
Raises:: RuntimeError – Raised if there’s an error converting a server-returned str-descriptor

Notes

We really don’t have an itemsize because these are variable length strings. In the future we could probably use this position to store the total bytes.

fullmatch(pattern: bytes | str_scalars) → Match[source]¶

Returns a match object where elements match only if the whole string matches the regular expression pattern

Parameters:: pattern (bytes or str_scalars) – Regex used to find matches
Returns:: Match object where elements match only if the whole string matches the regular expression pattern
Return type:: Match

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.fullmatch('_+')
<ak.Match object: matched=False; matched=True, span=(0, 4); matched=False;
matched=False; matched=False>

get_bytes() → pdarray[source]¶

Getter for the bytes component (uint8 pdarray) of this Strings.

Returns:: Pdarray of bytes of the string accessed
Return type:: pdarray, uint8

Example

>>> x = ak.array(['one', 'two', 'three'])
>>> x.get_bytes()
[111 110 101 0 116 119 111 0 116 104 114 101 101 0]

get_lengths() → pdarray[source]¶

Return the length of each string in the array.

Returns:: The length of each string
Return type:: pdarray, int
Raises:: RuntimeError – Raised if there is a server-side error thrown

get_offsets() → pdarray[source]¶

Getter for the offsets component (int64 pdarray) of this Strings.

Returns:: Pdarray of offsets of the string accessed
Return type:: pdarray, int64

Example

>>> x = ak.array(['one', 'two', 'three'])
>>> x.get_offsets()
[0 4 8]

get_prefixes(n: int_scalars, return_origins: bool = True, proper: bool = True) → Strings | Tuple[Strings, pdarray][source]¶

Return the n-long prefix of each string, where possible

Parameters:

n (int_scalars) – Length of prefix
return_origins (bool, default=True) – If True, return a logical index indicating which strings were long enough to return an n-prefix
proper (bool, default=True) – If True, only return proper prefixes, i.e. from strings that are at least n+1 long. If False, allow the entire string to be returned as a prefix.

Returns:

prefixes (Strings) – The array of n-character prefixes; the number of elements is the number of True values in the returned mask.
origin_indices (pdarray, bool) – Boolean array that is True where the string was long enough to return an n-character prefix, False otherwise.

get_suffixes(n: int_scalars, return_origins: bool = True, proper: bool = True) → Strings | Tuple[Strings, pdarray][source]¶

Return the n-long suffix of each string, where possible

Parameters:

n (int_scalars) – Length of suffix
return_origins (bool, default=True) – If True, return a logical index indicating which strings were long enough to return an n-suffix
proper (bool, default=True) – If True, only return proper suffixes, i.e. from strings that are at least n+1 long. If False, allow the entire string to be returned as a suffix.

Returns:

suffixes (Strings) – The array of n-character suffixes; the number of elements is the number of True values in the returned mask.
origin_indices (pdarray, bool) – Boolean array that is True where the string was long enough to return an n-character suffix, False otherwise.

group() → pdarray[source]¶

Return the permutation that groups the array, placing equivalent strings together. All instances of the same string are guaranteed to lie in one contiguous block of the permuted array, but the blocks are not necessarily ordered.

Returns:: The permutation that groups the array by value
Return type:: pdarray

See also

Strings.islower, Strings.isupper

Notes

If the arkouda server is compiled with “-sSegmentedString.useHash=true”, then arkouda uses 128-bit hash values to group strings, rather than sorting the strings directly. This method is fast, but the resulting permutation merely groups equivalent strings and does not sort them. If the “useHash” parameter is false, then a full sort is performed.

Raises:: RuntimeError – Raised if there is a server-side error in executing group request or creating the pdarray encapsulating the return message

hash() → Tuple[pdarray, pdarray][source]¶

Compute a 128-bit hash of each string.

Returns:: A tuple of two int64 pdarrays. The ith hash value is the concatenation of the ith values from each array.
Return type:: Tuple[pdarray,pdarray]

Notes

property inferred_type: Tuple[pdarray, pdarray]¶: Return a string of the type inferred from the values.

info() → str[source]¶

Returns a JSON formatted string containing information about all components of self

Parameters:: None
Returns:: JSON string containing information about all components of self
Return type:: str

is_registered() → np.bool_[source]¶

Return True iff the object is contained in the registry

Parameters:: None
Returns:: Indicates if the object is contained in the registry
Return type:: bool
Raises:: RuntimeError – Raised if there’s a server-side error thrown

isalnum() → pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is alphanumeric.

Returns:: True for elements that are alphanumeric, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> not_alnum = ak.array([f'%Strings {i}' for i in range(3)])
>>> alnum = ak.array([f'Strings{i}' for i in range(3)])
>>> strings = ak.concatenate([not_alnum, alnum])
>>> strings
array(['%Strings 0', '%Strings 1', '%Strings 2', 'Strings0', 'Strings1', 'Strings2'])
>>> strings.isalnum()
array([False False False True True True])

isalpha() → pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is alphabetic. This means there is at least one character, and all the characters are alphabetic.

Returns:: True for elements that are alphabetic, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> not_alpha = ak.array([f'%Strings {i}' for i in range(3)])
>>> alpha = ak.array(['StringA','StringB','StringC'])
>>> strings = ak.concatenate([not_alpha, alpha])
>>> strings
array(['%Strings 0', '%Strings 1', '%Strings 2', 'StringA', 'StringB', 'StringC'])
>>> strings.isalpha()
array([False False False True True True])

isdecimal() → pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings has all decimal characters.

Returns:: True for elements that are decimals, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.isdigit

Examples

>>> not_decimal = ak.array([f'Strings {i}' for i in range(3)])
>>> decimal = ak.array([f'12{i}' for i in range(3)])
>>> strings = ak.concatenate([not_decimal, decimal])
>>> strings
array(['Strings 0', 'Strings 1', 'Strings 2', '120', '121', '122'])
>>> strings.isdecimal()
array([False False False True True True])

Special Character Examples

>>> special_strings = ak.array(["3.14", "0", "²", "2³₇", "2³x₇"])
>>> special_strings
array(['3.14', '0', '²', '2³₇', '2³x₇'])
>>> special_strings.isdecimal()
array([False True False False False])

isdigit() → pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings has all digit characters.

Returns:: True for elements that are digits, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> not_digit = ak.array([f'Strings {i}' for i in range(3)])
>>> digit = ak.array([f'12{i}' for i in range(3)])
>>> strings = ak.concatenate([not_digit, digit])
>>> strings
array(['Strings 0', 'Strings 1', 'Strings 2', '120', '121', '122'])
>>> strings.isdigit()
array([False False False True True True])

Special Character Examples

>>> special_strings = ak.array(["3.14", "0", "²", "2³₇", "2³x₇"])
>>> special_strings
array(['3.14', '0', '²', '2³₇', '2³x₇'])
>>> special_strings.isdigit()
array([False True True True False])

isempty() → pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is empty.

True for elements that are the empty string, False otherwise

Returns:: True for elements that are digits, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> not_empty = ak.array([f'Strings {i}' for i in range(3)])
>>> empty = ak.array(['' for i in range(3)])
>>> strings = ak.concatenate([not_empty, empty])
>>> strings
array(['Strings 0', 'Strings 1', 'Strings 2', '', '', ''])
>>> strings.isempty()
array([False False False True True True])

islower() → pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is entirely lowercase

Returns:: True for elements that are entirely lowercase, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.isupper

Examples

>>> lower = ak.array([f'strings {i}' for i in range(3)])
>>> upper = ak.array([f'STRINGS {i}' for i in range(3)])
>>> strings = ak.concatenate([lower, upper])
>>> strings
array(['strings 0', 'strings 1', 'strings 2', 'STRINGS 0', 'STRINGS 1', 'STRINGS 2'])
>>> strings.islower()
array([True True True False False False])

isspace() → pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i has all whitespace characters (‘ ’, ‘\t’, ‘\n’, ‘\v’, ‘\f’, ‘\r’).

Returns:: True for elements that are whitespace, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> not_space = ak.array([f'Strings {i}' for i in range(3)])
>>> space = ak.array([' ', '\t', '\n', '\v', '\f', '\r', ' \t\n\v\f\r'])
>>> strings = ak.concatenate([not_space, space])
>>> strings
array(['Strings 0', 'Strings 1', 'Strings 2', ' ', 'u0009', 'n', 'u000B', 'u000C', 'u000D', ' u0009nu000Bu000Cu000D'])
>>> strings.isspace()
array([False False False True True True True True True True])

istitle() → pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is titlecase

Returns:: True for elements that are titlecase, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Examples

>>> mixed = ak.array([f'sTrINgs {i}' for i in range(3)])
>>> title = ak.array([f'Strings {i}' for i in range(3)])
>>> strings = ak.concatenate([mixed, title])
>>> strings
array(['sTrINgs 0', 'sTrINgs 1', 'sTrINgs 2', 'Strings 0', 'Strings 1', 'Strings 2'])
>>> strings.istitle()
array([False False False True True True])

isupper() → pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is entirely uppercase

Returns:: True for elements that are entirely uppercase, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.islower

Examples

>>> lower = ak.array([f'strings {i}' for i in range(3)])
>>> upper = ak.array([f'STRINGS {i}' for i in range(3)])
>>> strings = ak.concatenate([lower, upper])
>>> strings
array(['strings 0', 'strings 1', 'strings 2', 'STRINGS 0', 'STRINGS 1', 'STRINGS 2'])
>>> strings.isupper()
array([False False False True True True])

lower() → Strings[source]¶

Returns a new Strings with all uppercase characters from the original replaced with their lowercase equivalent

Returns:: Strings with all uppercase characters from the original replaced with their lowercase equivalent
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.upper

Examples

>>> strings = ak.array([f'StrINgS {i}' for i in range(5)])
>>> strings
array(['StrINgS 0', 'StrINgS 1', 'StrINgS 2', 'StrINgS 3', 'StrINgS 4'])
>>> strings.lower()
array(['strings 0', 'strings 1', 'strings 2', 'strings 3', 'strings 4'])

lstick(other: Strings, delimiter: bytes | str_scalars = '') → Strings[source]¶

Join the strings from another array onto the left of the strings of this array, optionally inserting a delimiter. Warning: This function is experimental and not guaranteed to work.

Parameters:

other (Strings) – The strings to join onto self’s strings
delimiter (bytes or str_scalars, default="") – String inserted between self and other

Returns:

The array of joined strings, as other + self

Return type:

Raises:

TypeError – Raised if the delimiter parameter is neither bytes nor a str or if the other parameter is not a Strings instance
RuntimeError – Raised if there is a server-side error thrown

See also

stick, peel, rpeel

Examples

>>> s = ak.array(['a', 'c', 'e'])
>>> t = ak.array(['b', 'd', 'f'])
>>> s.lstick(t, delimiter='.')
array(['b.a', 'd.c', 'f.e'])

match(pattern: bytes | str_scalars) → Match[source]¶

Returns a match object where elements match only if the beginning of the string matches the regular expression pattern

Parameters:: pattern (bytes or str_scalars) – Regex used to find matches
Returns:: Match object where elements match only if the beginning of the string matches the regular expression pattern
Return type:: Match

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.match('_+')
<ak.Match object: matched=False; matched=True, span=(0, 4); matched=False;
matched=True, span=(0, 2); matched=False>

objType(*args, **kwargs)¶

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

peel(delimiter: bytes | str_scalars, times: int_scalars = 1, includeDelimiter: bool = False, keepPartial: bool = False, fromRight: bool = False, regex: bool = False) → Tuple[Strings, Strings][source]¶

Peel off one or more delimited fields from each string (similar to string.partition), returning two new arrays of strings. Warning: This function is experimental and not guaranteed to work.

Parameters:

delimiter (bytes or str_scalars) – The separator where the split will occur
times (int_scalars, default=1) – The number of times the delimiter is sought, i.e. skip over the first (times-1) delimiters
includeDelimiter (bool, default=False) – If true, append the delimiter to the end of the first return array. By default, it is prepended to the beginning of the second return array.
keepPartial (bool, default=False) – If true, a string that does not contain <times> instances of the delimiter will be returned in the first array. By default, such strings are returned in the second array.
fromRight (bool, default=False) – If true, peel from the right instead of the left (see also rpeel)
regex (bool, default=False) – Indicates whether delimiter is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

left: Strings: The field(s) peeled from the end of each string (unless fromRight is true)
right: Strings: The remainder of each string after peeling (unless fromRight is true)

Return type:

Raises:

TypeError – Raised if the delimiter parameter is not byte or str_scalars, if times is not int64, or if includeDelimiter, keepPartial, or fromRight is not bool
ValueError – Raised if times is < 1 or if delimiter is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

rpeel, stick, lstick

Examples

>>> s = ak.array(['a.b', 'c.d', 'e.f.g'])
>>> s.peel('.')
(array(['a', 'c', 'e']), array(['b', 'd', 'f.g']))
>>> s.peel('.', includeDelimiter=True)
(array(['a.', 'c.', 'e.']), array(['b', 'd', 'f.g']))
>>> s.peel('.', times=2)
(array(['', '', 'e.f']), array(['a.b', 'c.d', 'g']))
>>> s.peel('.', times=2, keepPartial=True)
(array(['a.b', 'c.d', 'e.f']), array(['', '', 'g']))

pretty_print_info() → None[source]¶

Prints information about all components of self in a human readable format

Parameters:: None
Return type:: None

purge_cached_regex_patterns() → None[source]¶: purges cached regex patterns

regex_split(pattern: bytes | str_scalars, maxsplit: int = 0, return_segments: bool = False) → Strings | Tuple[source]¶

Returns a new Strings split by the occurrences of pattern. If maxsplit is nonzero, at most maxsplit splits occur

Parameters:

pattern (bytes or str_scalars) – Regex used to split strings into substrings
maxsplit (int, default=0) – The max number of pattern match occurences in each element to split. The default maxsplit=0 splits on all occurences
return_segments (bool, default=False) – If True, return mapping of original strings to first substring in return array.

Returns:

Strings – Substrings with pattern matches removed
pdarray, int64 (optional) – For each original string, the index of first corresponding substring in the return array

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.regex_split('_+', maxsplit=2, return_segments=True)
(array(['1', '2', '', '', '', '3', '', '4', '5____6___7', '']), array([0 3 5 6 9]))

register(user_defined_name: str) → Strings[source]¶

Register this Strings object with a user defined name in the arkouda server so it can be attached to later using Strings.attach() This is an in-place operation, registering a Strings object more than once will update the name in the registry and remove the previously registered name. A name can only be registered to one object at a time.

Parameters:

user_defined_name (str) – user defined name which the Strings object is to be registered under

Returns:

The same Strings object which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different objects with the same name.

Return type:

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Strings object with the user_defined_name If the user is attempting to register more than one object with the same name, the former should be unregistered first to free up the registration name.

See also

attach, unregister

Notes

Registered names/Strings objects in the server are immune to deletion until they are unregistered.

rpeel(delimiter: bytes | str_scalars, times: int_scalars = 1, includeDelimiter: bool = False, keepPartial: bool = False, regex: bool = False) → Tuple[Strings, Strings][source]¶

Peel off one or more delimited fields from the end of each string (similar to string.rpartition), returning two new arrays of strings. Warning: This function is experimental and not guaranteed to work.

Parameters:

delimiter (bytes or str_scalars) – The separator where the split will occur
times (int_scalars, default=1) – The number of times the delimiter is sought, i.e. skip over the last (times-1) delimiters
includeDelimiter (bool, default=False) – If true, prepend the delimiter to the start of the first return array. By default, it is appended to the end of the second return array.
keepPartial (bool, default=False) – If true, a string that does not contain <times> instances of the delimiter will be returned in the second array. By default, such strings are returned in the first array.
regex (bool, default=False) – Indicates whether delimiter is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

left: Strings: The remainder of the string after peeling
right: Strings: The field(s) that were peeled from the right of each string

Return type:

Strings.contains, Strings.endswith

Raises:

TypeError – Raised if the delimiter parameter is not bytes or str_scalars or if times is not int64
ValueError – Raised if times is < 1 or if delimiter is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

peel, stick, lstick

Examples

>>> s = ak.array(['a.b', 'c.d', 'e.f.g'])
>>> s.rpeel('.')
(array(['a', 'c', 'e.f']), array(['b', 'd', 'g']))

Compared against peel

>>> s.peel('.')
(array(['a', 'c', 'e']), array(['b', 'd', 'f.g']))

save(prefix_path: str, dataset: str = 'strings_array', mode: Literal['truncate', 'append'] = 'truncate', save_offsets: bool = True, compression: Literal['snappy', 'gzip', 'brotli', 'zstd', 'lz4'] | None = None, file_format: Literal['HDF5', 'Parquet'] = 'HDF5', file_type: Literal['single', 'distribute'] = 'distribute') → str[source]¶

DEPRECATED Save the Strings object to HDF5 or Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. HDF5 support single files, in which case the file name will only be that provided. Each locale saves its chunk of the array to its corresponding file.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str, default="strings_array") – The name of the Strings dataset to be written, defaults to strings_array
mode ({"truncate", "append"}, default = "truncate") – By default, truncate (overwrite) output files, if they exist. If ‘append’, create a new Strings dataset within existing files.
save_offsets (bool, default=True) – Defaults to True which will instruct the server to save the offsets array to HDF5 If False the offsets array will not be save and will be derived from the string values upon load/read. This is not supported for Parquet files.
compression ({"snappy", "gzip", "brotli", "zstd", "lz4"}, optional) – Sets the compression type used with Parquet files
file_format ({"HDF5", "Parquet"}, default = "HDF5") – By default, saved files will be written to the HDF5 file format. If ‘Parquet’, the files will be written to the Parquet file format. This is case insensitive.
file_type ({"single", "distribute"}, default = "distribute") – Default: Distribute Distribute the dataset over a file per locale. Single file will save the dataset to one file

Return type:

String message indicating result of save operation

Notes

search(pattern: bytes | str_scalars) → Match[source]¶

Returns a match object with the first location in each element where pattern produces a match. Elements match if any part of the string matches the regular expression pattern

Parameters:: pattern (bytes or str_scalars) – Regex used to find matches
Returns:: Match object where elements match if any part of the string matches the regular expression pattern
Return type:: Match

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.search('_+')
<ak.Match object: matched=True, span=(1, 2); matched=True, span=(0, 4);
matched=False; matched=True, span=(0, 2); matched=False>

split(delimiter: str, return_segments: bool = False, regex: bool = False) → Strings | Tuple[source]¶

Unpack delimiter-joined substrings into a flat array.

delimiter: str
Characters used to split strings into substrings

return_segmentsbool, default=False
If True, also return mapping of original strings to first substring in return array.

regexbool, default=False
Indicates whether delimiter is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Strings
Flattened substrings with delimiters removed

pdarray, int64 (optional)
For each original string, the index of first corresponding substring in the return array

peel, rpeel
>>> orig = ak.array(['one|two', 'three|four|five', 'six'])
>>> orig.split('|')
array(['one', 'two', 'three', 'four', 'five', 'six'])
>>> flat, mapping = orig.split('|', return_segments=True)
>>> mapping
array([0 2 5])
>>> under = ak.array(['one_two', 'three_____four____five', 'six'])
>>> under_split, under_map = under.split('_+', return_segments=True, regex=True)
>>> under_split
array(['one', 'two', 'three', 'four', 'five', 'six'])
>>> under_map
array([0 2 5])

startswith(substr: bytes | str_scalars, regex: bool = False) → pdarray[source]¶

Check whether each element starts with the given substring.

Parameters:

substr (bytes or str_scalars) – The prefix to search for
regex (bool, default=False) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

True for elements that start with substr, False otherwise

Return type:

pdarray, bool

Raises:

TypeError – Raised if the substr parameter is not a bytes ior str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Examples

>>> strings_end = ak.array([f'string {i}' for i in range(1, 6)])
>>> strings_end
array(['string 1', 'string 2', 'string 3', 'string 4', 'string 5'])
>>> strings_end.startswith('string')
array([True True True True True])
>>> strings_start = ak.array([f'{i} string' for i in range(1,6)])
>>> strings_start
array(['1 string', '2 string', '3 string', '4 string', '5 string'])
>>> strings_start.startswith('\d str', regex = True)
array([True True True True True])

stick(other: Strings, delimiter: bytes | str_scalars = '', toLeft: bool = False) → Strings[source]¶

Join the strings from another array onto one end of the strings of this array, optionally inserting a delimiter. Warning: This function is experimental and not guaranteed to work.

Parameters:

other (Strings) – The strings to join onto self’s strings
delimiter (bytes or str_scalars, default="") – String inserted between self and other
toLeft (bool, default=False) – If true, join other strings to the left of self. By default, other is joined to the right of self.

Returns:

The array of joined strings

Return type:

Raises:

TypeError – Raised if the delimiter parameter is not bytes or str_scalars or if the other parameter is not a Strings instance
ValueError – Raised if times is < 1
RuntimeError – Raised if there is a server-side error thrown

See also

lstick, peel, rpeel

Examples

>>> s = ak.array(['a', 'c', 'e'])
>>> t = ak.array(['b', 'd', 'f'])
>>> s.stick(t, delimiter='.')
array(['a.b', 'c.d', 'e.f'])

strip(chars: bytes | str_scalars | None = '') → Strings[source]¶

Returns a new Strings object with all leading and trailing occurrences of characters contained in chars removed. The chars argument is a string specifying the set of characters to be removed. If omitted, the chars argument defaults to removing whitespace. The chars argument is not a prefix or suffix; rather, all combinations of its values are stripped.

Parameters:: chars (bytes or str_scalars, optional) – the set of characters to be removed
Returns:: Strings object with the leading and trailing characters matching the set of characters in the chars argument removed
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> strings = ak.array(['Strings ', '  StringS  ', 'StringS   '])
>>> s = strings.strip()
>>> s
array(['Strings', 'StringS', 'StringS'])

>>> strings = ak.array(['Strings 1', '1 StringS  ', '  1StringS  12 '])
>>> s = strings.strip(' 12')
>>> s
array(['Strings', 'StringS', 'StringS'])

sub(pattern: bytes | str_scalars, repl: bytes | str_scalars, count: int = 0) → Strings[source]¶

Return new Strings obtained by replacing non-overlapping occurrences of pattern with the replacement repl. If count is nonzero, at most count substitutions occur

Parameters:

pattern (bytes or str_scalars) – The regex to substitue
repl (bytes or str_scalars) – The substring to replace pattern matches with
count (int, default=0) – The max number of pattern match occurences in each element to replace. The default count=0 replaces all occurences of pattern with repl

Returns:

Strings with pattern matches replaced

Return type:

Raises:

TypeError – Raised if pattern or repl are not bytes or str_scalars
ValueError – Raised if pattern is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Strings.subn

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.sub(pattern='_+', repl='-', count=2)
array(['1-2-', '-', '3', '-4-5____6___7', ''])

subn(pattern: bytes | str_scalars, repl: bytes | str_scalars, count: int = 0) → Tuple[source]¶

Perform the same operation as sub(), but return a tuple (new_Strings, number_of_substitions)

Parameters:

pattern (bytes or str_scalars) – The regex to substitue
repl (bytes or str_scalars) – The substring to replace pattern matches with
count (int, default=0) – The max number of pattern match occurences in each element to replace. The default count=0 replaces all occurences of pattern with repl

Returns:

Strings – Strings with pattern matches replaced
pdarray, int64 – The number of substitutions made for each element of Strings

Raises:

TypeError – Raised if pattern or repl are not bytes or str_scalars
ValueError – Raised if pattern is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Strings.sub

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.subn(pattern='_+', repl='-', count=2)
(array(['1-2-', '-', '3', '-4-5____6___7', '']), array([2 1 0 2 0]))

title() → Strings[source]¶

Returns a new Strings from the original replaced with their titlecase equivalent.

Returns:: Strings from the original replaced with their titlecase equivalent.
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown.

See also

Strings.lower, String.upper

Examples

>>> strings = ak.array([f'StrINgS {i}' for i in range(5)])
>>> strings
array(['StrINgS 0', 'StrINgS 1', 'StrINgS 2', 'StrINgS 3', 'StrINgS 4'])
>>> strings.title()
array(['Strings 0', 'Strings 1', 'Strings 2', 'Strings 3', 'Strings 4'])

to_csv(prefix_path: str, dataset: str = 'strings_array', col_delim: str = ',', overwrite: bool = False) → str[source]¶

Write Strings to CSV file(s). File will contain a single column with the Strings data. All CSV Files written by Arkouda include a header denoting data types of the columns. Unlike other file formats, CSV files store Strings as their UTF-8 format instead of storing bytes as uint(8).

Parameters:

prefix_path (str) – The filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
dataset (str, default="strings_array") – Column name to save the Strings under. Defaults to “strings_array”.
col_delim (str, default=",") – Defaults to “,”. Value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
overwrite (bool, default=False) – Defaults to False. If True, any existing files matching your provided prefix_path will be overwritten. If False, an error will be returned if existing files are found.

Returns:

response message

Return type:

str

Raises:

ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server

Notes

CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for column names and data
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (\n) at this time.

to_hdf(prefix_path: str, dataset: str = 'strings_array', mode: Literal['truncate', 'append'] = 'truncate', save_offsets: bool = True, file_type: Literal['single', 'distribute'] = 'distribute') → str[source]¶

Save the Strings object to HDF5. The object can be saved to a collection of files or single file.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str, default="strings_array") – The name of the Strings dataset to be written, defaults to strings_array
mode ({"truncate", "append"}, default = "truncate") – By default, truncate (overwrite) output files, if they exist. If ‘append’, create a new Strings dataset within existing files.
save_offsets (bool, default=True) – Defaults to True which will instruct the server to save the offsets array to HDF5 If False the offsets array will not be save and will be derived from the string values upon load/read.
file_type ({"single", "distribute"}, default = "distribute") – Default: Distribute Distribute the dataset over a file per locale. Single file will save the dataset to one file

Return type:

String message indicating result of save operation

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

Parquet files do not store the segments, only the values.
Strings state is saved as two datasets within an hdf5 group: one for the string characters and one for the segments corresponding to the start of each string
the hdf5 group is named via the dataset parameter.
The prefix_path must be visible to the arkouda server and the user must have write permission.
Output files have names of the form <prefix_path>_LOCALE, where  ranges from 0 to numLocales for file_type=’distribute’. Otherwise, the file name will be prefix_path.
If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, a RuntimeError will result.
Any file extension can be used.The file I/O does not rely on the extension to determine the file format.

See also

to_list() → list[source]¶

Convert the SegString to a list, transferring data from the arkouda server to Python. If the SegString exceeds a built-in size limit, a RuntimeError is raised.

Returns:: A list with the same strings as this SegString
Return type:: list

Notes

See also

Examples

>>> a = ak.array(["hello", "my", "world"])
>>> a.to_list()
['hello', 'my', 'world']
>>> type(a.to_list())
<class 'list'>

to_ndarray() → np.ndarray[source]¶

Convert the array to a np.ndarray, transferring array data from the arkouda server to Python. If the array exceeds a built-in size limit, a RuntimeError is raised.

Returns:: A numpy ndarray with the same strings as this array
Return type:: np.ndarray

Notes

See also

Examples

>>> a = ak.array(["hello", "my", "world"])
>>> a.to_ndarray()
array(['hello', 'my', 'world'], dtype='<U5')
>>> type(a.to_ndarray())
<class 'numpy.ndarray'>

to_parquet(prefix_path: str, dataset: str = 'strings_array', mode: Literal['truncate', 'append'] = 'truncate', compression: Literal['snappy', 'gzip', 'brotli', 'zstd', 'lz4'] | None = None) → str[source]¶

Save the Strings object to Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the array to its corresponding file. :param prefix_path: Directory and filename prefix that all output files share :type prefix_path: str :param dataset: Name of the dataset to create in files (must not already exist) :type dataset: str, default=”strings_array” :param mode: By default, truncate (overwrite) output files, if they exist.

If ‘append’, attempt to create new dataset in existing files.

Parameters:: compression ({"snappy", "gzip", "brotli", "zstd", "lz4"}, optional) – Sets the compression type used with Parquet files
Return type:: string message indicating result of save operation
Raises:: RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

The prefix_path must be visible to the arkouda server and the user must

transfer(hostname: str, port: int_scalars) → str | memoryview[source]¶

Sends a Strings object to a different Arkouda server

Parameters:

hostname (str) – The hostname where the Arkouda server intended to receive the Strings object is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().

Return type:

A message indicating a complete transfer

Raises:

ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype

unregister() → None[source]¶

Unregister a Strings object in the arkouda server which was previously registered using register() and/or attached to using attach()

Return type:: None
Raises:: RuntimeError – Raised if the server could not find the internal name/symbol to remove

See also

register, attach

Notes

Registered names/Strings objects in the server are immune to deletion until they are unregistered.

unregister_strings_by_name(user_defined_name: str) → None[source]¶

Unregister a Strings object in the arkouda server previously registered via register()

Parameters:: user_defined_name (str) – The registered name of the Strings object

update_hdf(prefix_path: str, dataset: str = 'strings_array', save_offsets: bool = True, repack: bool = True) → str[source]¶

Overwrite the dataset with the name provided with this Strings object. If the dataset does not exist it is added

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str, default="strings_array") – Name of the dataset to create in files
save_offsets (bool, default=True) – Defaults to True which will instruct the server to save the offsets array to HDF5 If False the offsets array will not be save and will be derived from the string values upon load/read.
repack (bool, default=True) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.

Return type:

str - success message if successful

Raises:

RuntimeError – Raised if a server-side error is thrown saving the Strings object

Notes

If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added

upper() → Strings[source]¶

Returns a new Strings with all lowercase characters from the original replaced with their uppercase equivalent

Returns:: Strings with all lowercase characters from the original replaced with their uppercase equivalent
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.lower

Examples

>>> strings = ak.array([f'StrINgS {i}' for i in range(5)])
>>> strings
array(['StrINgS 0', 'StrINgS 1', 'StrINgS 2', 'StrINgS 3', 'StrINgS 4'])
>>> strings.upper()
array(['STRINGS 0', 'STRINGS 1', 'STRINGS 2', 'STRINGS 3', 'STRINGS 4'])

class arkouda.Strings(strings_pdarray: arkouda.numpy.pdarrayclass.pdarray, bytes_size: arkouda.numpy.dtypes.int_scalars)[source]¶

Represents an array of strings whose data resides on the arkouda server. The user should not call this class directly; rather its instances are created by other arkouda functions.

entry¶

Encapsulation of a Segmented Strings array contained on the arkouda server. This is a composite of

offsets array: starting indices for each string

bytes array: raw bytes of all strings joined by nulls

Type:: pdarray

size¶

The number of strings in the array

Type:: int_scalars

nbytes¶

The total number of bytes in all strings

Type:: int_scalars

ndim¶

The rank of the array (currently only rank 1 arrays supported)

Type:: int_scalars

shape¶

The sizes of each dimension of the array

Type:: tuple

dtype¶

The dtype is ak.str

Type:: dtype

logger¶

Used for all logging operations

Type:: ArkoudaLogger

Notes

Strings is composed of two pdarrays: (1) offsets, which contains the starting indices for each string and (2) bytes, which contains the raw bytes of all strings, delimited by nulls.

BinOps¶

astype(dtype: numpy.dtype | str) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Cast values of Strings object to provided dtype

Parameters:: dtype (np.dtype or str) – Dtype to cast to
Returns:: An arkouda pdarray with values converted to the specified data type
Return type:: ak.pdarray

Notes

This is essentially shorthand for ak.cast(x, ‘<dtype>’) where x is a pdarray.

static attach(user_defined_name: str) → Strings[source]¶

class method to return a Strings object attached to the registered name in the arkouda server which was registered using register()

Parameters:: user_defined_name (str) – user defined name which the Strings object was registered under
Returns:: the Strings object registered with user_defined_name in the arkouda server
Return type:: Strings object
Raises:: TypeError – Raised if user_defined_name is not a str

See also

register, unregister

Notes

Registered names/Strings objects in the server are immune to deletion until they are unregistered.

cached_regex_patterns() → List[source]¶: Returns the regex patterns for which Match objects have been cached

capitalize() → Strings[source]¶

Returns a new Strings from the original replaced with the first letter capitilzed and the remaining letters lowercase.

Returns:: Strings from the original replaced with the capitalized equivalent.
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown.

See also

Strings.lower, String.upper, String.title

Examples

>>> strings = ak.array([f'StrINgS aRe Here {i}' for i in range(5)])
>>> strings
array(['StrINgS aRe Here 0', 'StrINgS aRe Here 1', 'StrINgS aRe Here 2', 'StrINgS aRe Here 3', 'StrINgS aRe Here 4'])
>>> strings.title()
array(['Strings Are Here 0', 'Strings Are Here 1', 'Strings Are Here 2', 'Strings Are Here 3', 'Strings Are Here 4'])

contains(substr: bytes | arkouda.numpy.dtypes.str_scalars, regex: bool = False) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Check whether each element contains the given substring.

Parameters:

substr (bytes or str_scalars) – The substring in the form of string or byte array to search for
regex (bool, default=False) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

True for elements that contain substr, False otherwise

Return type:

pdarray, bool

Raises:

TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

Examples

>>> strings = ak.array([f'{i} string {i}' for i in range(1, 6)])
>>> strings
array(['1 string 1', '2 string 2', '3 string 3', '4 string 4', '5 string 5'])
>>> strings.contains('string')
array([True True True True True])
>>> strings.contains('string \d', regex=True)
array([True True True True True])

decode(fromEncoding: str, toEncoding: str = 'UTF-8') → Strings[source]¶

Return a new strings object in fromEncoding, expecting that the current Strings is encoded in toEncoding

Parameters:

fromEncoding (str) – The current encoding of the strings object
toEncoding (str, default="UTF-8") – The encoding that the strings will be converted to, default to UTF-8

Returns:

A new Strings object in toEncoding

Return type:

Raises:

RuntimeError – Raised if there is a server-side error thrown

dtype¶

encode(toEncoding: str, fromEncoding: str = 'UTF-8') → Strings[source]¶

Return a new strings object in toEncoding, expecting that the current Strings is encoded in fromEncoding

Parameters:

toEncoding (str) – The encoding that the strings will be converted to
fromEncoding (str, default="UTF-8") – The current encoding of the strings object, default to UTF-8

Returns:

A new Strings object in toEncoding

Return type:

Strings.findall, Strings.match

Raises:

RuntimeError – Raised if there is a server-side error thrown

endswith(substr: bytes | arkouda.numpy.dtypes.str_scalars, regex: bool = False) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Check whether each element ends with the given substring.

Parameters:

substr (bytes or str_scalars) – The suffix to search for
regex (bool, default=False) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

True for elements that end with substr, False otherwise

Return type:

pdarray, bool

Raises:

TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

Examples

>>> strings_start = ak.array([f'{i} string' for i in range(1,6)])
>>> strings_start
array(['1 string', '2 string', '3 string', '4 string', '5 string'])
>>> strings_start.endswith('ing')
array([True True True True True])
>>> strings_end = ak.array([f'string {i}' for i in range(1, 6)])
>>> strings_end
array(['string 1', 'string 2', 'string 3', 'string 4', 'string 5'])
>>> strings_end.endswith('ing \d', regex = True)
array([True True True True True])

entry: arkouda.numpy.pdarrayclass.pdarray¶

equals(other: Any) → arkouda.numpy.dtypes.bool_scalars[source]¶

Whether Strings are the same size and all entries are equal.

Parameters:: other (Any) – object to compare.
Returns:: True if the Strings are the same, o.w. False.
Return type:: bool

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> s = ak.array(["a", "b", "c"])
>>> s_cpy = ak.array(["a", "b", "c"])
>>> s.equals(s_cpy)
True
>>> s2 = ak.array(["a", "x", "c"])
>>> s.equals(s2)
False

find_locations(pattern: bytes | arkouda.numpy.dtypes.str_scalars) → Tuple[arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.pdarrayclass.pdarray][source]¶

Finds pattern matches and returns pdarrays containing the number, start postitions, and lengths of matches

Parameters:

pattern (bytes or str_scalars) – The regex pattern used to find matches

Returns:

pdarray, int64 – For each original string, the number of pattern matches
pdarray, int64 – The start positons of pattern matches
pdarray, int64 – The lengths of pattern matches

Raises:

TypeError – Raised if the pattern parameter is not bytes or str_scalars
ValueError – Raised if pattern is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Examples

>>> strings = ak.array([f'{i} string {i}' for i in range(1, 6)])
>>> num_matches, starts, lens = strings.find_locations('\d')
>>> num_matches
array([2 2 2 2 2])
>>> starts
array([0 9 0 9 0 9 0 9 0 9])
>>> lens
array([1 1 1 1 1 1 1 1 1 1])

findall(pattern: bytes | arkouda.numpy.dtypes.str_scalars, return_match_origins: bool = False) → Strings | Tuple[source]¶

Return a new Strings containg all non-overlapping matches of pattern

Parameters:

pattern (bytes or str_scalars) – Regex used to find matches
return_match_origins (bool, default=False) – If True, return a pdarray containing the index of the original string each pattern match is from

Returns:

Strings – Strings object containing only pattern matches
pdarray, int64 (optional) – The index of the original string each pattern match is from

Raises:

TypeError – Raised if the pattern parameter is not bytes or str_scalars
ValueError – Raised if pattern is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Strings.find_locations

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.findall('_+', return_match_origins=True)
(array(['_', '___', '____', '__', '___', '____', '___']), array([0 0 1 3 3 3 3]))

flatten() → Strings[source]¶

Return a copy of the array collapsed into one dimension.

Return type:: A copy of the input array, flattened to one dimension.

Note

As multidimensional Strings are currently supported, flatten on a Strings object will always return itself.

static from_parts(offset_attrib: arkouda.numpy.pdarrayclass.pdarray | str, bytes_attrib: arkouda.numpy.pdarrayclass.pdarray | str) → Strings[source]¶

Factory method for creating a Strings object from an Arkouda server response where the arrays are separate components.

Parameters:

offset_attrib (pdarray or str) – the array containing the offsets
bytes_attrib (pdarray or str) – the array containing the string values

Returns:

object representing a segmented strings array on the server

Return type:

Raises:

RuntimeError – Raised if there’s an error converting a server-returned str-descriptor

Notes

static from_return_msg(rep_msg: str) → Strings[source]¶

Factory method for creating a Strings object from an Arkouda server response message

Parameters:: rep_msg (str) – Server response message currently of form created name type size ndim shape itemsize+created bytes.size 1234
Returns:: object representing a segmented strings array on the server
Return type:: Strings
Raises:: RuntimeError – Raised if there’s an error converting a server-returned str-descriptor

Notes

We really don’t have an itemsize because these are variable length strings. In the future we could probably use this position to store the total bytes.

fullmatch(pattern: bytes | arkouda.numpy.dtypes.str_scalars) → arkouda.match.Match[source]¶

Returns a match object where elements match only if the whole string matches the regular expression pattern

Parameters:: pattern (bytes or str_scalars) – Regex used to find matches
Returns:: Match object where elements match only if the whole string matches the regular expression pattern
Return type:: Match

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.fullmatch('_+')
<ak.Match object: matched=False; matched=True, span=(0, 4); matched=False;
matched=False; matched=False>

get_bytes() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Getter for the bytes component (uint8 pdarray) of this Strings.

Returns:: Pdarray of bytes of the string accessed
Return type:: pdarray, uint8

Example

>>> x = ak.array(['one', 'two', 'three'])
>>> x.get_bytes()
[111 110 101 0 116 119 111 0 116 104 114 101 101 0]

get_lengths() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the length of each string in the array.

Returns:: The length of each string
Return type:: pdarray, int
Raises:: RuntimeError – Raised if there is a server-side error thrown

get_offsets() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Getter for the offsets component (int64 pdarray) of this Strings.

Returns:: Pdarray of offsets of the string accessed
Return type:: pdarray, int64

Example

>>> x = ak.array(['one', 'two', 'three'])
>>> x.get_offsets()
[0 4 8]

get_prefixes(n: arkouda.numpy.dtypes.int_scalars, return_origins: bool = True, proper: bool = True) → Strings | Tuple[Strings, arkouda.numpy.pdarrayclass.pdarray][source]¶

Return the n-long prefix of each string, where possible

Parameters:

n (int_scalars) – Length of prefix
return_origins (bool, default=True) – If True, return a logical index indicating which strings were long enough to return an n-prefix
proper (bool, default=True) – If True, only return proper prefixes, i.e. from strings that are at least n+1 long. If False, allow the entire string to be returned as a prefix.

Returns:

prefixes (Strings) – The array of n-character prefixes; the number of elements is the number of True values in the returned mask.
origin_indices (pdarray, bool) – Boolean array that is True where the string was long enough to return an n-character prefix, False otherwise.

get_suffixes(n: arkouda.numpy.dtypes.int_scalars, return_origins: bool = True, proper: bool = True) → Strings | Tuple[Strings, arkouda.numpy.pdarrayclass.pdarray][source]¶

Return the n-long suffix of each string, where possible

Parameters:

n (int_scalars) – Length of suffix
return_origins (bool, default=True) – If True, return a logical index indicating which strings were long enough to return an n-suffix
proper (bool, default=True) – If True, only return proper suffixes, i.e. from strings that are at least n+1 long. If False, allow the entire string to be returned as a suffix.

Returns:

suffixes (Strings) – The array of n-character suffixes; the number of elements is the number of True values in the returned mask.
origin_indices (pdarray, bool) – Boolean array that is True where the string was long enough to return an n-character suffix, False otherwise.

group() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns:: The permutation that groups the array by value
Return type:: pdarray

See also

Strings.islower, Strings.isupper

Notes

Raises:: RuntimeError – Raised if there is a server-side error in executing group request or creating the pdarray encapsulating the return message

hash() → Tuple[arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.pdarrayclass.pdarray][source]¶

Compute a 128-bit hash of each string.

Returns:: A tuple of two int64 pdarrays. The ith hash value is the concatenation of the ith values from each array.
Return type:: Tuple[pdarray,pdarray]

Notes

property inferred_type: str¶: Return a string of the type inferred from the values.

info() → str[source]¶

Returns a JSON formatted string containing information about all components of self

Parameters:: None
Returns:: JSON string containing information about all components of self
Return type:: str

is_registered() → numpy.bool_[source]¶

Return True iff the object is contained in the registry

Parameters:: None
Returns:: Indicates if the object is contained in the registry
Return type:: bool
Raises:: RuntimeError – Raised if there’s a server-side error thrown

isalnum() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is alphanumeric.

Returns:: True for elements that are alphanumeric, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> not_alnum = ak.array([f'%Strings {i}' for i in range(3)])
>>> alnum = ak.array([f'Strings{i}' for i in range(3)])
>>> strings = ak.concatenate([not_alnum, alnum])
>>> strings
array(['%Strings 0', '%Strings 1', '%Strings 2', 'Strings0', 'Strings1', 'Strings2'])
>>> strings.isalnum()
array([False False False True True True])

isalpha() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is alphabetic. This means there is at least one character, and all the characters are alphabetic.

Returns:: True for elements that are alphabetic, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> not_alpha = ak.array([f'%Strings {i}' for i in range(3)])
>>> alpha = ak.array(['StringA','StringB','StringC'])
>>> strings = ak.concatenate([not_alpha, alpha])
>>> strings
array(['%Strings 0', '%Strings 1', '%Strings 2', 'StringA', 'StringB', 'StringC'])
>>> strings.isalpha()
array([False False False True True True])

isdecimal() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings has all decimal characters.

Returns:: True for elements that are decimals, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.isdigit

Examples

>>> not_decimal = ak.array([f'Strings {i}' for i in range(3)])
>>> decimal = ak.array([f'12{i}' for i in range(3)])
>>> strings = ak.concatenate([not_decimal, decimal])
>>> strings
array(['Strings 0', 'Strings 1', 'Strings 2', '120', '121', '122'])
>>> strings.isdecimal()
array([False False False True True True])

Special Character Examples

>>> special_strings = ak.array(["3.14", "0", "²", "2³₇", "2³x₇"])
>>> special_strings
array(['3.14', '0', '²', '2³₇', '2³x₇'])
>>> special_strings.isdecimal()
array([False True False False False])

isdigit() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings has all digit characters.

Returns:: True for elements that are digits, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> not_digit = ak.array([f'Strings {i}' for i in range(3)])
>>> digit = ak.array([f'12{i}' for i in range(3)])
>>> strings = ak.concatenate([not_digit, digit])
>>> strings
array(['Strings 0', 'Strings 1', 'Strings 2', '120', '121', '122'])
>>> strings.isdigit()
array([False False False True True True])

Special Character Examples

>>> special_strings = ak.array(["3.14", "0", "²", "2³₇", "2³x₇"])
>>> special_strings
array(['3.14', '0', '²', '2³₇', '2³x₇'])
>>> special_strings.isdigit()
array([False True True True False])

isempty() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is empty.

True for elements that are the empty string, False otherwise

Returns:: True for elements that are digits, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> not_empty = ak.array([f'Strings {i}' for i in range(3)])
>>> empty = ak.array(['' for i in range(3)])
>>> strings = ak.concatenate([not_empty, empty])
>>> strings
array(['Strings 0', 'Strings 1', 'Strings 2', '', '', ''])
>>> strings.isempty()
array([False False False True True True])

islower() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is entirely lowercase

Returns:: True for elements that are entirely lowercase, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.isupper

Examples

>>> lower = ak.array([f'strings {i}' for i in range(3)])
>>> upper = ak.array([f'STRINGS {i}' for i in range(3)])
>>> strings = ak.concatenate([lower, upper])
>>> strings
array(['strings 0', 'strings 1', 'strings 2', 'STRINGS 0', 'STRINGS 1', 'STRINGS 2'])
>>> strings.islower()
array([True True True False False False])

isspace() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i has all whitespace characters (‘ ’, ‘\t’, ‘\n’, ‘\v’, ‘\f’, ‘\r’).

Returns:: True for elements that are whitespace, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> not_space = ak.array([f'Strings {i}' for i in range(3)])
>>> space = ak.array([' ', '\t', '\n', '\v', '\f', '\r', ' \t\n\v\f\r'])
>>> strings = ak.concatenate([not_space, space])
>>> strings
array(['Strings 0', 'Strings 1', 'Strings 2', ' ', 'u0009', 'n', 'u000B', 'u000C', 'u000D', ' u0009nu000Bu000Cu000D'])
>>> strings.isspace()
array([False False False True True True True True True True])

istitle() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is titlecase

Returns:: True for elements that are titlecase, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Examples

>>> mixed = ak.array([f'sTrINgs {i}' for i in range(3)])
>>> title = ak.array([f'Strings {i}' for i in range(3)])
>>> strings = ak.concatenate([mixed, title])
>>> strings
array(['sTrINgs 0', 'sTrINgs 1', 'sTrINgs 2', 'Strings 0', 'Strings 1', 'Strings 2'])
>>> strings.istitle()
array([False False False True True True])

isupper() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is entirely uppercase

Returns:: True for elements that are entirely uppercase, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.islower

Examples

>>> lower = ak.array([f'strings {i}' for i in range(3)])
>>> upper = ak.array([f'STRINGS {i}' for i in range(3)])
>>> strings = ak.concatenate([lower, upper])
>>> strings
array(['strings 0', 'strings 1', 'strings 2', 'STRINGS 0', 'STRINGS 1', 'STRINGS 2'])
>>> strings.isupper()
array([False False False True True True])

logger¶

lower() → Strings[source]¶

Returns a new Strings with all uppercase characters from the original replaced with their lowercase equivalent

Returns:: Strings with all uppercase characters from the original replaced with their lowercase equivalent
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.upper

Examples

>>> strings = ak.array([f'StrINgS {i}' for i in range(5)])
>>> strings
array(['StrINgS 0', 'StrINgS 1', 'StrINgS 2', 'StrINgS 3', 'StrINgS 4'])
>>> strings.lower()
array(['strings 0', 'strings 1', 'strings 2', 'strings 3', 'strings 4'])

lstick(other: Strings, delimiter: bytes | arkouda.numpy.dtypes.str_scalars = '') → Strings[source]¶

Join the strings from another array onto the left of the strings of this array, optionally inserting a delimiter. Warning: This function is experimental and not guaranteed to work.

Parameters:

other (Strings) – The strings to join onto self’s strings
delimiter (bytes or str_scalars, default="") – String inserted between self and other

Returns:

The array of joined strings, as other + self

Return type:

Raises:

TypeError – Raised if the delimiter parameter is neither bytes nor a str or if the other parameter is not a Strings instance
RuntimeError – Raised if there is a server-side error thrown

See also

stick, peel, rpeel

Examples

>>> s = ak.array(['a', 'c', 'e'])
>>> t = ak.array(['b', 'd', 'f'])
>>> s.lstick(t, delimiter='.')
array(['b.a', 'd.c', 'f.e'])

match(pattern: bytes | arkouda.numpy.dtypes.str_scalars) → arkouda.match.Match[source]¶

Returns a match object where elements match only if the beginning of the string matches the regular expression pattern

Parameters:: pattern (bytes or str_scalars) – Regex used to find matches
Returns:: Match object where elements match only if the beginning of the string matches the regular expression pattern
Return type:: Match

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.match('_+')
<ak.Match object: matched=False; matched=True, span=(0, 4); matched=False;
matched=True, span=(0, 2); matched=False>

objType = 'Strings'¶

peel(delimiter: bytes | arkouda.numpy.dtypes.str_scalars, times: arkouda.numpy.dtypes.int_scalars = 1, includeDelimiter: bool = False, keepPartial: bool = False, fromRight: bool = False, regex: bool = False) → Tuple[Strings, Strings][source]¶

Peel off one or more delimited fields from each string (similar to string.partition), returning two new arrays of strings. Warning: This function is experimental and not guaranteed to work.

Parameters:

delimiter (bytes or str_scalars) – The separator where the split will occur
times (int_scalars, default=1) – The number of times the delimiter is sought, i.e. skip over the first (times-1) delimiters
includeDelimiter (bool, default=False) – If true, append the delimiter to the end of the first return array. By default, it is prepended to the beginning of the second return array.
keepPartial (bool, default=False) – If true, a string that does not contain <times> instances of the delimiter will be returned in the first array. By default, such strings are returned in the second array.
fromRight (bool, default=False) – If true, peel from the right instead of the left (see also rpeel)
regex (bool, default=False) – Indicates whether delimiter is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

left: Strings: The field(s) peeled from the end of each string (unless fromRight is true)
right: Strings: The remainder of each string after peeling (unless fromRight is true)

Return type:

Raises:

TypeError – Raised if the delimiter parameter is not byte or str_scalars, if times is not int64, or if includeDelimiter, keepPartial, or fromRight is not bool
ValueError – Raised if times is < 1 or if delimiter is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

rpeel, stick, lstick

Examples

>>> s = ak.array(['a.b', 'c.d', 'e.f.g'])
>>> s.peel('.')
(array(['a', 'c', 'e']), array(['b', 'd', 'f.g']))
>>> s.peel('.', includeDelimiter=True)
(array(['a.', 'c.', 'e.']), array(['b', 'd', 'f.g']))
>>> s.peel('.', times=2)
(array(['', '', 'e.f']), array(['a.b', 'c.d', 'g']))
>>> s.peel('.', times=2, keepPartial=True)
(array(['a.b', 'c.d', 'e.f']), array(['', '', 'g']))

pretty_print_info() → None[source]¶

Prints information about all components of self in a human readable format

Parameters:: None
Return type:: None

purge_cached_regex_patterns() → None[source]¶: purges cached regex patterns

regex_split(pattern: bytes | arkouda.numpy.dtypes.str_scalars, maxsplit: int = 0, return_segments: bool = False) → Strings | Tuple[source]¶

Returns a new Strings split by the occurrences of pattern. If maxsplit is nonzero, at most maxsplit splits occur

Parameters:

pattern (bytes or str_scalars) – Regex used to split strings into substrings
maxsplit (int, default=0) – The max number of pattern match occurences in each element to split. The default maxsplit=0 splits on all occurences
return_segments (bool, default=False) – If True, return mapping of original strings to first substring in return array.

Returns:

Strings – Substrings with pattern matches removed
pdarray, int64 (optional) – For each original string, the index of first corresponding substring in the return array

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.regex_split('_+', maxsplit=2, return_segments=True)
(array(['1', '2', '', '', '', '3', '', '4', '5____6___7', '']), array([0 3 5 6 9]))

register(user_defined_name: str) → Strings[source]¶

Parameters:

user_defined_name (str) – user defined name which the Strings object is to be registered under

Returns:

Return type:

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Strings object with the user_defined_name If the user is attempting to register more than one object with the same name, the former should be unregistered first to free up the registration name.

See also

attach, unregister

Notes

Registered names/Strings objects in the server are immune to deletion until they are unregistered.

registered_name: str | None = None¶

rpeel(delimiter: bytes | arkouda.numpy.dtypes.str_scalars, times: arkouda.numpy.dtypes.int_scalars = 1, includeDelimiter: bool = False, keepPartial: bool = False, regex: bool = False) → Tuple[Strings, Strings][source]¶

Parameters:

delimiter (bytes or str_scalars) – The separator where the split will occur
times (int_scalars, default=1) – The number of times the delimiter is sought, i.e. skip over the last (times-1) delimiters
includeDelimiter (bool, default=False) – If true, prepend the delimiter to the start of the first return array. By default, it is appended to the end of the second return array.
keepPartial (bool, default=False) – If true, a string that does not contain <times> instances of the delimiter will be returned in the second array. By default, such strings are returned in the first array.
regex (bool, default=False) – Indicates whether delimiter is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

left: Strings: The remainder of the string after peeling
right: Strings: The field(s) that were peeled from the right of each string

Return type:

Strings.contains, Strings.endswith

Raises:

TypeError – Raised if the delimiter parameter is not bytes or str_scalars or if times is not int64
ValueError – Raised if times is < 1 or if delimiter is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

peel, stick, lstick

Examples

>>> s = ak.array(['a.b', 'c.d', 'e.f.g'])
>>> s.rpeel('.')
(array(['a', 'c', 'e.f']), array(['b', 'd', 'g']))

Compared against peel

>>> s.peel('.')
(array(['a', 'c', 'e']), array(['b', 'd', 'f.g']))

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str, default="strings_array") – The name of the Strings dataset to be written, defaults to strings_array
mode ({"truncate", "append"}, default = "truncate") – By default, truncate (overwrite) output files, if they exist. If ‘append’, create a new Strings dataset within existing files.
save_offsets (bool, default=True) – Defaults to True which will instruct the server to save the offsets array to HDF5 If False the offsets array will not be save and will be derived from the string values upon load/read. This is not supported for Parquet files.
compression ({"snappy", "gzip", "brotli", "zstd", "lz4"}, optional) – Sets the compression type used with Parquet files
file_format ({"HDF5", "Parquet"}, default = "HDF5") – By default, saved files will be written to the HDF5 file format. If ‘Parquet’, the files will be written to the Parquet file format. This is case insensitive.
file_type ({"single", "distribute"}, default = "distribute") – Default: Distribute Distribute the dataset over a file per locale. Single file will save the dataset to one file

Return type:

String message indicating result of save operation

Notes

search(pattern: bytes | arkouda.numpy.dtypes.str_scalars) → arkouda.match.Match[source]¶

Returns a match object with the first location in each element where pattern produces a match. Elements match if any part of the string matches the regular expression pattern

Parameters:: pattern (bytes or str_scalars) – Regex used to find matches
Returns:: Match object where elements match if any part of the string matches the regular expression pattern
Return type:: Match

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.search('_+')
<ak.Match object: matched=True, span=(1, 2); matched=True, span=(0, 4);
matched=False; matched=True, span=(0, 2); matched=False>

size: arkouda.numpy.dtypes.int_scalars¶

split(delimiter: str, return_segments: bool = False, regex: bool = False) → Strings | Tuple[source]¶

Unpack delimiter-joined substrings into a flat array.

Parameters:

delimiter (str) – Characters used to split strings into substrings
return_segments (bool, default=False) – If True, also return mapping of original strings to first substring in return array.
regex (bool, default=False) – Indicates whether delimiter is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

Strings – Flattened substrings with delimiters removed
pdarray, int64 (optional) – For each original string, the index of first corresponding substring in the return array

See also

peel, rpeel

Examples

>>> orig = ak.array(['one|two', 'three|four|five', 'six'])
>>> orig.split('|')
array(['one', 'two', 'three', 'four', 'five', 'six'])
>>> flat, mapping = orig.split('|', return_segments=True)
>>> mapping
array([0 2 5])
>>> under = ak.array(['one_two', 'three_____four____five', 'six'])
>>> under_split, under_map = under.split('_+', return_segments=True, regex=True)
>>> under_split
array(['one', 'two', 'three', 'four', 'five', 'six'])
>>> under_map
array([0 2 5])

startswith(substr: bytes | arkouda.numpy.dtypes.str_scalars, regex: bool = False) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Check whether each element starts with the given substring.

Parameters:

substr (bytes or str_scalars) – The prefix to search for
regex (bool, default=False) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

True for elements that start with substr, False otherwise

Return type:

pdarray, bool

Raises:

TypeError – Raised if the substr parameter is not a bytes ior str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Examples

>>> strings_end = ak.array([f'string {i}' for i in range(1, 6)])
>>> strings_end
array(['string 1', 'string 2', 'string 3', 'string 4', 'string 5'])
>>> strings_end.startswith('string')
array([True True True True True])
>>> strings_start = ak.array([f'{i} string' for i in range(1,6)])
>>> strings_start
array(['1 string', '2 string', '3 string', '4 string', '5 string'])
>>> strings_start.startswith('\d str', regex = True)
array([True True True True True])

stick(other: Strings, delimiter: bytes | arkouda.numpy.dtypes.str_scalars = '', toLeft: bool = False) → Strings[source]¶

Join the strings from another array onto one end of the strings of this array, optionally inserting a delimiter. Warning: This function is experimental and not guaranteed to work.

Parameters:

other (Strings) – The strings to join onto self’s strings
delimiter (bytes or str_scalars, default="") – String inserted between self and other
toLeft (bool, default=False) – If true, join other strings to the left of self. By default, other is joined to the right of self.

Returns:

The array of joined strings

Return type:

Raises:

TypeError – Raised if the delimiter parameter is not bytes or str_scalars or if the other parameter is not a Strings instance
ValueError – Raised if times is < 1
RuntimeError – Raised if there is a server-side error thrown

See also

lstick, peel, rpeel

Examples

>>> s = ak.array(['a', 'c', 'e'])
>>> t = ak.array(['b', 'd', 'f'])
>>> s.stick(t, delimiter='.')
array(['a.b', 'c.d', 'e.f'])

strip(chars: bytes | arkouda.numpy.dtypes.str_scalars | None = '') → Strings[source]¶

Parameters:: chars (bytes or str_scalars, optional) – the set of characters to be removed
Returns:: Strings object with the leading and trailing characters matching the set of characters in the chars argument removed
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> strings = ak.array(['Strings ', '  StringS  ', 'StringS   '])
>>> s = strings.strip()
>>> s
array(['Strings', 'StringS', 'StringS'])

>>> strings = ak.array(['Strings 1', '1 StringS  ', '  1StringS  12 '])
>>> s = strings.strip(' 12')
>>> s
array(['Strings', 'StringS', 'StringS'])

sub(pattern: bytes | arkouda.numpy.dtypes.str_scalars, repl: bytes | arkouda.numpy.dtypes.str_scalars, count: int = 0) → Strings[source]¶

Return new Strings obtained by replacing non-overlapping occurrences of pattern with the replacement repl. If count is nonzero, at most count substitutions occur

Parameters:

pattern (bytes or str_scalars) – The regex to substitue
repl (bytes or str_scalars) – The substring to replace pattern matches with
count (int, default=0) – The max number of pattern match occurences in each element to replace. The default count=0 replaces all occurences of pattern with repl

Returns:

Strings with pattern matches replaced

Return type:

Raises:

TypeError – Raised if pattern or repl are not bytes or str_scalars
ValueError – Raised if pattern is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Strings.subn

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.sub(pattern='_+', repl='-', count=2)
array(['1-2-', '-', '3', '-4-5____6___7', ''])

subn(pattern: bytes | arkouda.numpy.dtypes.str_scalars, repl: bytes | arkouda.numpy.dtypes.str_scalars, count: int = 0) → Tuple[source]¶

Perform the same operation as sub(), but return a tuple (new_Strings, number_of_substitions)

Parameters:

pattern (bytes or str_scalars) – The regex to substitue
repl (bytes or str_scalars) – The substring to replace pattern matches with
count (int, default=0) – The max number of pattern match occurences in each element to replace. The default count=0 replaces all occurences of pattern with repl

Returns:

Strings – Strings with pattern matches replaced
pdarray, int64 – The number of substitutions made for each element of Strings

Raises:

TypeError – Raised if pattern or repl are not bytes or str_scalars
ValueError – Raised if pattern is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Strings.sub

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.subn(pattern='_+', repl='-', count=2)
(array(['1-2-', '-', '3', '-4-5____6___7', '']), array([2 1 0 2 0]))

title() → Strings[source]¶

Returns a new Strings from the original replaced with their titlecase equivalent.

Returns:: Strings from the original replaced with their titlecase equivalent.
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown.

See also

Strings.lower, String.upper

Examples

>>> strings = ak.array([f'StrINgS {i}' for i in range(5)])
>>> strings
array(['StrINgS 0', 'StrINgS 1', 'StrINgS 2', 'StrINgS 3', 'StrINgS 4'])
>>> strings.title()
array(['Strings 0', 'Strings 1', 'Strings 2', 'Strings 3', 'Strings 4'])

to_csv(prefix_path: str, dataset: str = 'strings_array', col_delim: str = ',', overwrite: bool = False) → str[source]¶

Parameters:

prefix_path (str) – The filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
dataset (str, default="strings_array") – Column name to save the Strings under. Defaults to “strings_array”.
col_delim (str, default=",") – Defaults to “,”. Value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
overwrite (bool, default=False) – Defaults to False. If True, any existing files matching your provided prefix_path will be overwritten. If False, an error will be returned if existing files are found.

Returns:

response message

Return type:

str

Raises:

ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server

Notes

CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for column names and data
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (\n) at this time.

Save the Strings object to HDF5. The object can be saved to a collection of files or single file.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str, default="strings_array") – The name of the Strings dataset to be written, defaults to strings_array
mode ({"truncate", "append"}, default = "truncate") – By default, truncate (overwrite) output files, if they exist. If ‘append’, create a new Strings dataset within existing files.
save_offsets (bool, default=True) – Defaults to True which will instruct the server to save the offsets array to HDF5 If False the offsets array will not be save and will be derived from the string values upon load/read.
file_type ({"single", "distribute"}, default = "distribute") – Default: Distribute Distribute the dataset over a file per locale. Single file will save the dataset to one file

Return type:

String message indicating result of save operation

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

Parquet files do not store the segments, only the values.
Strings state is saved as two datasets within an hdf5 group: one for the string characters and one for the segments corresponding to the start of each string
the hdf5 group is named via the dataset parameter.
The prefix_path must be visible to the arkouda server and the user must have write permission.
Output files have names of the form <prefix_path>_LOCALE, where  ranges from 0 to numLocales for file_type=’distribute’. Otherwise, the file name will be prefix_path.
If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, a RuntimeError will result.
Any file extension can be used.The file I/O does not rely on the extension to determine the file format.

See also

to_list() → list[source]¶

Convert the SegString to a list, transferring data from the arkouda server to Python. If the SegString exceeds a built-in size limit, a RuntimeError is raised.

Returns:: A list with the same strings as this SegString
Return type:: list

Notes

See also

Examples

>>> a = ak.array(["hello", "my", "world"])
>>> a.to_list()
['hello', 'my', 'world']
>>> type(a.to_list())
<class 'list'>

to_ndarray() → numpy.ndarray[source]¶

Convert the array to a np.ndarray, transferring array data from the arkouda server to Python. If the array exceeds a built-in size limit, a RuntimeError is raised.

Returns:: A numpy ndarray with the same strings as this array
Return type:: np.ndarray

Notes

See also

Examples

>>> a = ak.array(["hello", "my", "world"])
>>> a.to_ndarray()
array(['hello', 'my', 'world'], dtype='<U5')
>>> type(a.to_ndarray())
<class 'numpy.ndarray'>

If ‘append’, attempt to create new dataset in existing files.

Parameters:: compression ({"snappy", "gzip", "brotli", "zstd", "lz4"}, optional) – Sets the compression type used with Parquet files
Return type:: string message indicating result of save operation
Raises:: RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

The prefix_path must be visible to the arkouda server and the user must

transfer(hostname: str, port: arkouda.numpy.dtypes.int_scalars) → str | memoryview[source]¶

Sends a Strings object to a different Arkouda server

Parameters:

hostname (str) – The hostname where the Arkouda server intended to receive the Strings object is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().

Return type:

A message indicating a complete transfer

Raises:

ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype

unregister() → None[source]¶

Unregister a Strings object in the arkouda server which was previously registered using register() and/or attached to using attach()

Return type:: None
Raises:: RuntimeError – Raised if the server could not find the internal name/symbol to remove

See also

register, attach

Notes

Registered names/Strings objects in the server are immune to deletion until they are unregistered.

static unregister_strings_by_name(user_defined_name: str) → None[source]¶

Unregister a Strings object in the arkouda server previously registered via register()

Parameters:: user_defined_name (str) – The registered name of the Strings object

update_hdf(prefix_path: str, dataset: str = 'strings_array', save_offsets: bool = True, repack: bool = True) → str[source]¶

Overwrite the dataset with the name provided with this Strings object. If the dataset does not exist it is added

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str, default="strings_array") – Name of the dataset to create in files
save_offsets (bool, default=True) – Defaults to True which will instruct the server to save the offsets array to HDF5 If False the offsets array will not be save and will be derived from the string values upon load/read.
repack (bool, default=True) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.

Return type:

str - success message if successful

Raises:

RuntimeError – Raised if a server-side error is thrown saving the Strings object

Notes

If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added

upper() → Strings[source]¶

Returns a new Strings with all lowercase characters from the original replaced with their uppercase equivalent

Returns:: Strings with all lowercase characters from the original replaced with their uppercase equivalent
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.lower

Examples

>>> strings = ak.array([f'StrINgS {i}' for i in range(5)])
>>> strings
array(['StrINgS 0', 'StrINgS 1', 'StrINgS 2', 'StrINgS 3', 'StrINgS 4'])
>>> strings.upper()
array(['STRINGS 0', 'STRINGS 1', 'STRINGS 2', 'STRINGS 3', 'STRINGS 4'])

class arkouda.Strings(strings_pdarray: arkouda.numpy.pdarrayclass.pdarray, bytes_size: arkouda.numpy.dtypes.int_scalars)[source]¶

Represents an array of strings whose data resides on the arkouda server. The user should not call this class directly; rather its instances are created by other arkouda functions.

entry¶

Encapsulation of a Segmented Strings array contained on the arkouda server. This is a composite of

offsets array: starting indices for each string

bytes array: raw bytes of all strings joined by nulls

Type:: pdarray

size¶

The number of strings in the array

Type:: int_scalars

nbytes¶

The total number of bytes in all strings

Type:: int_scalars

ndim¶

The rank of the array (currently only rank 1 arrays supported)

Type:: int_scalars

shape¶

The sizes of each dimension of the array

Type:: tuple

dtype¶

The dtype is ak.str

Type:: dtype

logger¶

Used for all logging operations

Type:: ArkoudaLogger

Notes

Strings is composed of two pdarrays: (1) offsets, which contains the starting indices for each string and (2) bytes, which contains the raw bytes of all strings, delimited by nulls.

BinOps¶

astype(dtype: numpy.dtype | str) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Cast values of Strings object to provided dtype

Parameters:: dtype (np.dtype or str) – Dtype to cast to
Returns:: An arkouda pdarray with values converted to the specified data type
Return type:: ak.pdarray

Notes

This is essentially shorthand for ak.cast(x, ‘<dtype>’) where x is a pdarray.

static attach(user_defined_name: str) → Strings[source]¶

class method to return a Strings object attached to the registered name in the arkouda server which was registered using register()

Parameters:: user_defined_name (str) – user defined name which the Strings object was registered under
Returns:: the Strings object registered with user_defined_name in the arkouda server
Return type:: Strings object
Raises:: TypeError – Raised if user_defined_name is not a str

See also

register, unregister

Notes

Registered names/Strings objects in the server are immune to deletion until they are unregistered.

cached_regex_patterns() → List[source]¶: Returns the regex patterns for which Match objects have been cached

capitalize() → Strings[source]¶

Returns a new Strings from the original replaced with the first letter capitilzed and the remaining letters lowercase.

Returns:: Strings from the original replaced with the capitalized equivalent.
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown.

See also

Strings.lower, String.upper, String.title

Examples

>>> strings = ak.array([f'StrINgS aRe Here {i}' for i in range(5)])
>>> strings
array(['StrINgS aRe Here 0', 'StrINgS aRe Here 1', 'StrINgS aRe Here 2', 'StrINgS aRe Here 3', 'StrINgS aRe Here 4'])
>>> strings.title()
array(['Strings Are Here 0', 'Strings Are Here 1', 'Strings Are Here 2', 'Strings Are Here 3', 'Strings Are Here 4'])

contains(substr: bytes | arkouda.numpy.dtypes.str_scalars, regex: bool = False) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Check whether each element contains the given substring.

Parameters:

substr (bytes or str_scalars) – The substring in the form of string or byte array to search for
regex (bool, default=False) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

True for elements that contain substr, False otherwise

Return type:

pdarray, bool

Raises:

TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

Examples

>>> strings = ak.array([f'{i} string {i}' for i in range(1, 6)])
>>> strings
array(['1 string 1', '2 string 2', '3 string 3', '4 string 4', '5 string 5'])
>>> strings.contains('string')
array([True True True True True])
>>> strings.contains('string \d', regex=True)
array([True True True True True])

decode(fromEncoding: str, toEncoding: str = 'UTF-8') → Strings[source]¶

Return a new strings object in fromEncoding, expecting that the current Strings is encoded in toEncoding

Parameters:

fromEncoding (str) – The current encoding of the strings object
toEncoding (str, default="UTF-8") – The encoding that the strings will be converted to, default to UTF-8

Returns:

A new Strings object in toEncoding

Return type:

Raises:

RuntimeError – Raised if there is a server-side error thrown

dtype¶

encode(toEncoding: str, fromEncoding: str = 'UTF-8') → Strings[source]¶

Return a new strings object in toEncoding, expecting that the current Strings is encoded in fromEncoding

Parameters:

toEncoding (str) – The encoding that the strings will be converted to
fromEncoding (str, default="UTF-8") – The current encoding of the strings object, default to UTF-8

Returns:

A new Strings object in toEncoding

Return type:

Strings.findall, Strings.match

Raises:

RuntimeError – Raised if there is a server-side error thrown

endswith(substr: bytes | arkouda.numpy.dtypes.str_scalars, regex: bool = False) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Check whether each element ends with the given substring.

Parameters:

substr (bytes or str_scalars) – The suffix to search for
regex (bool, default=False) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

True for elements that end with substr, False otherwise

Return type:

pdarray, bool

Raises:

TypeError – Raised if the substr parameter is not bytes or str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

Examples

>>> strings_start = ak.array([f'{i} string' for i in range(1,6)])
>>> strings_start
array(['1 string', '2 string', '3 string', '4 string', '5 string'])
>>> strings_start.endswith('ing')
array([True True True True True])
>>> strings_end = ak.array([f'string {i}' for i in range(1, 6)])
>>> strings_end
array(['string 1', 'string 2', 'string 3', 'string 4', 'string 5'])
>>> strings_end.endswith('ing \d', regex = True)
array([True True True True True])

entry: arkouda.numpy.pdarrayclass.pdarray¶

equals(other: Any) → arkouda.numpy.dtypes.bool_scalars[source]¶

Whether Strings are the same size and all entries are equal.

Parameters:: other (Any) – object to compare.
Returns:: True if the Strings are the same, o.w. False.
Return type:: bool

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> s = ak.array(["a", "b", "c"])
>>> s_cpy = ak.array(["a", "b", "c"])
>>> s.equals(s_cpy)
True
>>> s2 = ak.array(["a", "x", "c"])
>>> s.equals(s2)
False

find_locations(pattern: bytes | arkouda.numpy.dtypes.str_scalars) → Tuple[arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.pdarrayclass.pdarray][source]¶

Finds pattern matches and returns pdarrays containing the number, start postitions, and lengths of matches

Parameters:

pattern (bytes or str_scalars) – The regex pattern used to find matches

Returns:

pdarray, int64 – For each original string, the number of pattern matches
pdarray, int64 – The start positons of pattern matches
pdarray, int64 – The lengths of pattern matches

Raises:

TypeError – Raised if the pattern parameter is not bytes or str_scalars
ValueError – Raised if pattern is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Examples

>>> strings = ak.array([f'{i} string {i}' for i in range(1, 6)])
>>> num_matches, starts, lens = strings.find_locations('\d')
>>> num_matches
array([2 2 2 2 2])
>>> starts
array([0 9 0 9 0 9 0 9 0 9])
>>> lens
array([1 1 1 1 1 1 1 1 1 1])

findall(pattern: bytes | arkouda.numpy.dtypes.str_scalars, return_match_origins: bool = False) → Strings | Tuple[source]¶

Return a new Strings containg all non-overlapping matches of pattern

Parameters:

pattern (bytes or str_scalars) – Regex used to find matches
return_match_origins (bool, default=False) – If True, return a pdarray containing the index of the original string each pattern match is from

Returns:

Strings – Strings object containing only pattern matches
pdarray, int64 (optional) – The index of the original string each pattern match is from

Raises:

TypeError – Raised if the pattern parameter is not bytes or str_scalars
ValueError – Raised if pattern is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Strings.find_locations

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.findall('_+', return_match_origins=True)
(array(['_', '___', '____', '__', '___', '____', '___']), array([0 0 1 3 3 3 3]))

flatten() → Strings[source]¶

Return a copy of the array collapsed into one dimension.

Return type:: A copy of the input array, flattened to one dimension.

Note

As multidimensional Strings are currently supported, flatten on a Strings object will always return itself.

static from_parts(offset_attrib: arkouda.numpy.pdarrayclass.pdarray | str, bytes_attrib: arkouda.numpy.pdarrayclass.pdarray | str) → Strings[source]¶

Factory method for creating a Strings object from an Arkouda server response where the arrays are separate components.

Parameters:

offset_attrib (pdarray or str) – the array containing the offsets
bytes_attrib (pdarray or str) – the array containing the string values

Returns:

object representing a segmented strings array on the server

Return type:

Raises:

RuntimeError – Raised if there’s an error converting a server-returned str-descriptor

Notes

static from_return_msg(rep_msg: str) → Strings[source]¶

Factory method for creating a Strings object from an Arkouda server response message

Parameters:: rep_msg (str) – Server response message currently of form created name type size ndim shape itemsize+created bytes.size 1234
Returns:: object representing a segmented strings array on the server
Return type:: Strings
Raises:: RuntimeError – Raised if there’s an error converting a server-returned str-descriptor

Notes

We really don’t have an itemsize because these are variable length strings. In the future we could probably use this position to store the total bytes.

fullmatch(pattern: bytes | arkouda.numpy.dtypes.str_scalars) → arkouda.match.Match[source]¶

Returns a match object where elements match only if the whole string matches the regular expression pattern

Parameters:: pattern (bytes or str_scalars) – Regex used to find matches
Returns:: Match object where elements match only if the whole string matches the regular expression pattern
Return type:: Match

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.fullmatch('_+')
<ak.Match object: matched=False; matched=True, span=(0, 4); matched=False;
matched=False; matched=False>

get_bytes() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Getter for the bytes component (uint8 pdarray) of this Strings.

Returns:: Pdarray of bytes of the string accessed
Return type:: pdarray, uint8

Example

>>> x = ak.array(['one', 'two', 'three'])
>>> x.get_bytes()
[111 110 101 0 116 119 111 0 116 104 114 101 101 0]

get_lengths() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the length of each string in the array.

Returns:: The length of each string
Return type:: pdarray, int
Raises:: RuntimeError – Raised if there is a server-side error thrown

get_offsets() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Getter for the offsets component (int64 pdarray) of this Strings.

Returns:: Pdarray of offsets of the string accessed
Return type:: pdarray, int64

Example

>>> x = ak.array(['one', 'two', 'three'])
>>> x.get_offsets()
[0 4 8]

get_prefixes(n: arkouda.numpy.dtypes.int_scalars, return_origins: bool = True, proper: bool = True) → Strings | Tuple[Strings, arkouda.numpy.pdarrayclass.pdarray][source]¶

Return the n-long prefix of each string, where possible

Parameters:

n (int_scalars) – Length of prefix
return_origins (bool, default=True) – If True, return a logical index indicating which strings were long enough to return an n-prefix
proper (bool, default=True) – If True, only return proper prefixes, i.e. from strings that are at least n+1 long. If False, allow the entire string to be returned as a prefix.

Returns:

prefixes (Strings) – The array of n-character prefixes; the number of elements is the number of True values in the returned mask.
origin_indices (pdarray, bool) – Boolean array that is True where the string was long enough to return an n-character prefix, False otherwise.

get_suffixes(n: arkouda.numpy.dtypes.int_scalars, return_origins: bool = True, proper: bool = True) → Strings | Tuple[Strings, arkouda.numpy.pdarrayclass.pdarray][source]¶

Return the n-long suffix of each string, where possible

Parameters:

n (int_scalars) – Length of suffix
return_origins (bool, default=True) – If True, return a logical index indicating which strings were long enough to return an n-suffix
proper (bool, default=True) – If True, only return proper suffixes, i.e. from strings that are at least n+1 long. If False, allow the entire string to be returned as a suffix.

Returns:

suffixes (Strings) – The array of n-character suffixes; the number of elements is the number of True values in the returned mask.
origin_indices (pdarray, bool) – Boolean array that is True where the string was long enough to return an n-character suffix, False otherwise.

group() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns:: The permutation that groups the array by value
Return type:: pdarray

See also

Strings.islower, Strings.isupper

Notes

Raises:: RuntimeError – Raised if there is a server-side error in executing group request or creating the pdarray encapsulating the return message

hash() → Tuple[arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.pdarrayclass.pdarray][source]¶

Compute a 128-bit hash of each string.

Returns:: A tuple of two int64 pdarrays. The ith hash value is the concatenation of the ith values from each array.
Return type:: Tuple[pdarray,pdarray]

Notes

property inferred_type: str¶: Return a string of the type inferred from the values.

info() → str[source]¶

Returns a JSON formatted string containing information about all components of self

Parameters:: None
Returns:: JSON string containing information about all components of self
Return type:: str

is_registered() → numpy.bool_[source]¶

Return True iff the object is contained in the registry

Parameters:: None
Returns:: Indicates if the object is contained in the registry
Return type:: bool
Raises:: RuntimeError – Raised if there’s a server-side error thrown

isalnum() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is alphanumeric.

Returns:: True for elements that are alphanumeric, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> not_alnum = ak.array([f'%Strings {i}' for i in range(3)])
>>> alnum = ak.array([f'Strings{i}' for i in range(3)])
>>> strings = ak.concatenate([not_alnum, alnum])
>>> strings
array(['%Strings 0', '%Strings 1', '%Strings 2', 'Strings0', 'Strings1', 'Strings2'])
>>> strings.isalnum()
array([False False False True True True])

isalpha() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is alphabetic. This means there is at least one character, and all the characters are alphabetic.

Returns:: True for elements that are alphabetic, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> not_alpha = ak.array([f'%Strings {i}' for i in range(3)])
>>> alpha = ak.array(['StringA','StringB','StringC'])
>>> strings = ak.concatenate([not_alpha, alpha])
>>> strings
array(['%Strings 0', '%Strings 1', '%Strings 2', 'StringA', 'StringB', 'StringC'])
>>> strings.isalpha()
array([False False False True True True])

isdecimal() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings has all decimal characters.

Returns:: True for elements that are decimals, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.isdigit

Examples

>>> not_decimal = ak.array([f'Strings {i}' for i in range(3)])
>>> decimal = ak.array([f'12{i}' for i in range(3)])
>>> strings = ak.concatenate([not_decimal, decimal])
>>> strings
array(['Strings 0', 'Strings 1', 'Strings 2', '120', '121', '122'])
>>> strings.isdecimal()
array([False False False True True True])

Special Character Examples

>>> special_strings = ak.array(["3.14", "0", "²", "2³₇", "2³x₇"])
>>> special_strings
array(['3.14', '0', '²', '2³₇', '2³x₇'])
>>> special_strings.isdecimal()
array([False True False False False])

isdigit() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings has all digit characters.

Returns:: True for elements that are digits, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> not_digit = ak.array([f'Strings {i}' for i in range(3)])
>>> digit = ak.array([f'12{i}' for i in range(3)])
>>> strings = ak.concatenate([not_digit, digit])
>>> strings
array(['Strings 0', 'Strings 1', 'Strings 2', '120', '121', '122'])
>>> strings.isdigit()
array([False False False True True True])

Special Character Examples

>>> special_strings = ak.array(["3.14", "0", "²", "2³₇", "2³x₇"])
>>> special_strings
array(['3.14', '0', '²', '2³₇', '2³x₇'])
>>> special_strings.isdigit()
array([False True True True False])

isempty() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is empty.

True for elements that are the empty string, False otherwise

Returns:: True for elements that are digits, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> not_empty = ak.array([f'Strings {i}' for i in range(3)])
>>> empty = ak.array(['' for i in range(3)])
>>> strings = ak.concatenate([not_empty, empty])
>>> strings
array(['Strings 0', 'Strings 1', 'Strings 2', '', '', ''])
>>> strings.isempty()
array([False False False True True True])

islower() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is entirely lowercase

Returns:: True for elements that are entirely lowercase, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.isupper

Examples

>>> lower = ak.array([f'strings {i}' for i in range(3)])
>>> upper = ak.array([f'STRINGS {i}' for i in range(3)])
>>> strings = ak.concatenate([lower, upper])
>>> strings
array(['strings 0', 'strings 1', 'strings 2', 'STRINGS 0', 'STRINGS 1', 'STRINGS 2'])
>>> strings.islower()
array([True True True False False False])

isspace() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i has all whitespace characters (‘ ’, ‘\t’, ‘\n’, ‘\v’, ‘\f’, ‘\r’).

Returns:: True for elements that are whitespace, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> not_space = ak.array([f'Strings {i}' for i in range(3)])
>>> space = ak.array([' ', '\t', '\n', '\v', '\f', '\r', ' \t\n\v\f\r'])
>>> strings = ak.concatenate([not_space, space])
>>> strings
array(['Strings 0', 'Strings 1', 'Strings 2', ' ', 'u0009', 'n', 'u000B', 'u000C', 'u000D', ' u0009nu000Bu000Cu000D'])
>>> strings.isspace()
array([False False False True True True True True True True])

istitle() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is titlecase

Returns:: True for elements that are titlecase, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Examples

>>> mixed = ak.array([f'sTrINgs {i}' for i in range(3)])
>>> title = ak.array([f'Strings {i}' for i in range(3)])
>>> strings = ak.concatenate([mixed, title])
>>> strings
array(['sTrINgs 0', 'sTrINgs 1', 'sTrINgs 2', 'Strings 0', 'Strings 1', 'Strings 2'])
>>> strings.istitle()
array([False False False True True True])

isupper() → arkouda.numpy.pdarrayclass.pdarray[source]¶

Returns a boolean pdarray where index i indicates whether string i of the Strings is entirely uppercase

Returns:: True for elements that are entirely uppercase, False otherwise
Return type:: pdarray, bool
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.islower

Examples

>>> lower = ak.array([f'strings {i}' for i in range(3)])
>>> upper = ak.array([f'STRINGS {i}' for i in range(3)])
>>> strings = ak.concatenate([lower, upper])
>>> strings
array(['strings 0', 'strings 1', 'strings 2', 'STRINGS 0', 'STRINGS 1', 'STRINGS 2'])
>>> strings.isupper()
array([False False False True True True])

logger¶

lower() → Strings[source]¶

Returns a new Strings with all uppercase characters from the original replaced with their lowercase equivalent

Returns:: Strings with all uppercase characters from the original replaced with their lowercase equivalent
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.upper

Examples

>>> strings = ak.array([f'StrINgS {i}' for i in range(5)])
>>> strings
array(['StrINgS 0', 'StrINgS 1', 'StrINgS 2', 'StrINgS 3', 'StrINgS 4'])
>>> strings.lower()
array(['strings 0', 'strings 1', 'strings 2', 'strings 3', 'strings 4'])

lstick(other: Strings, delimiter: bytes | arkouda.numpy.dtypes.str_scalars = '') → Strings[source]¶

Join the strings from another array onto the left of the strings of this array, optionally inserting a delimiter. Warning: This function is experimental and not guaranteed to work.

Parameters:

other (Strings) – The strings to join onto self’s strings
delimiter (bytes or str_scalars, default="") – String inserted between self and other

Returns:

The array of joined strings, as other + self

Return type:

Raises:

TypeError – Raised if the delimiter parameter is neither bytes nor a str or if the other parameter is not a Strings instance
RuntimeError – Raised if there is a server-side error thrown

See also

stick, peel, rpeel

Examples

>>> s = ak.array(['a', 'c', 'e'])
>>> t = ak.array(['b', 'd', 'f'])
>>> s.lstick(t, delimiter='.')
array(['b.a', 'd.c', 'f.e'])

match(pattern: bytes | arkouda.numpy.dtypes.str_scalars) → arkouda.match.Match[source]¶

Returns a match object where elements match only if the beginning of the string matches the regular expression pattern

Parameters:: pattern (bytes or str_scalars) – Regex used to find matches
Returns:: Match object where elements match only if the beginning of the string matches the regular expression pattern
Return type:: Match

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.match('_+')
<ak.Match object: matched=False; matched=True, span=(0, 4); matched=False;
matched=True, span=(0, 2); matched=False>

objType = 'Strings'¶

Peel off one or more delimited fields from each string (similar to string.partition), returning two new arrays of strings. Warning: This function is experimental and not guaranteed to work.

Parameters:

delimiter (bytes or str_scalars) – The separator where the split will occur
times (int_scalars, default=1) – The number of times the delimiter is sought, i.e. skip over the first (times-1) delimiters
includeDelimiter (bool, default=False) – If true, append the delimiter to the end of the first return array. By default, it is prepended to the beginning of the second return array.
keepPartial (bool, default=False) – If true, a string that does not contain <times> instances of the delimiter will be returned in the first array. By default, such strings are returned in the second array.
fromRight (bool, default=False) – If true, peel from the right instead of the left (see also rpeel)
regex (bool, default=False) – Indicates whether delimiter is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

left: Strings: The field(s) peeled from the end of each string (unless fromRight is true)
right: Strings: The remainder of each string after peeling (unless fromRight is true)

Return type:

Raises:

TypeError – Raised if the delimiter parameter is not byte or str_scalars, if times is not int64, or if includeDelimiter, keepPartial, or fromRight is not bool
ValueError – Raised if times is < 1 or if delimiter is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

rpeel, stick, lstick

Examples

>>> s = ak.array(['a.b', 'c.d', 'e.f.g'])
>>> s.peel('.')
(array(['a', 'c', 'e']), array(['b', 'd', 'f.g']))
>>> s.peel('.', includeDelimiter=True)
(array(['a.', 'c.', 'e.']), array(['b', 'd', 'f.g']))
>>> s.peel('.', times=2)
(array(['', '', 'e.f']), array(['a.b', 'c.d', 'g']))
>>> s.peel('.', times=2, keepPartial=True)
(array(['a.b', 'c.d', 'e.f']), array(['', '', 'g']))

pretty_print_info() → None[source]¶

Prints information about all components of self in a human readable format

Parameters:: None
Return type:: None

purge_cached_regex_patterns() → None[source]¶: purges cached regex patterns

regex_split(pattern: bytes | arkouda.numpy.dtypes.str_scalars, maxsplit: int = 0, return_segments: bool = False) → Strings | Tuple[source]¶

Returns a new Strings split by the occurrences of pattern. If maxsplit is nonzero, at most maxsplit splits occur

Parameters:

pattern (bytes or str_scalars) – Regex used to split strings into substrings
maxsplit (int, default=0) – The max number of pattern match occurences in each element to split. The default maxsplit=0 splits on all occurences
return_segments (bool, default=False) – If True, return mapping of original strings to first substring in return array.

Returns:

Strings – Substrings with pattern matches removed
pdarray, int64 (optional) – For each original string, the index of first corresponding substring in the return array

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.regex_split('_+', maxsplit=2, return_segments=True)
(array(['1', '2', '', '', '', '3', '', '4', '5____6___7', '']), array([0 3 5 6 9]))

register(user_defined_name: str) → Strings[source]¶

Parameters:

user_defined_name (str) – user defined name which the Strings object is to be registered under

Returns:

Return type:

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the Strings object with the user_defined_name If the user is attempting to register more than one object with the same name, the former should be unregistered first to free up the registration name.

See also

attach, unregister

Notes

Registered names/Strings objects in the server are immune to deletion until they are unregistered.

registered_name: str | None = None¶

Parameters:

delimiter (bytes or str_scalars) – The separator where the split will occur
times (int_scalars, default=1) – The number of times the delimiter is sought, i.e. skip over the last (times-1) delimiters
includeDelimiter (bool, default=False) – If true, prepend the delimiter to the start of the first return array. By default, it is appended to the end of the second return array.
keepPartial (bool, default=False) – If true, a string that does not contain <times> instances of the delimiter will be returned in the second array. By default, such strings are returned in the first array.
regex (bool, default=False) – Indicates whether delimiter is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

left: Strings: The remainder of the string after peeling
right: Strings: The field(s) that were peeled from the right of each string

Return type:

Strings.contains, Strings.endswith

Raises:

TypeError – Raised if the delimiter parameter is not bytes or str_scalars or if times is not int64
ValueError – Raised if times is < 1 or if delimiter is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

peel, stick, lstick

Examples

>>> s = ak.array(['a.b', 'c.d', 'e.f.g'])
>>> s.rpeel('.')
(array(['a', 'c', 'e.f']), array(['b', 'd', 'g']))

Compared against peel

>>> s.peel('.')
(array(['a', 'c', 'e']), array(['b', 'd', 'f.g']))

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str, default="strings_array") – The name of the Strings dataset to be written, defaults to strings_array
mode ({"truncate", "append"}, default = "truncate") – By default, truncate (overwrite) output files, if they exist. If ‘append’, create a new Strings dataset within existing files.
save_offsets (bool, default=True) – Defaults to True which will instruct the server to save the offsets array to HDF5 If False the offsets array will not be save and will be derived from the string values upon load/read. This is not supported for Parquet files.
compression ({"snappy", "gzip", "brotli", "zstd", "lz4"}, optional) – Sets the compression type used with Parquet files
file_format ({"HDF5", "Parquet"}, default = "HDF5") – By default, saved files will be written to the HDF5 file format. If ‘Parquet’, the files will be written to the Parquet file format. This is case insensitive.
file_type ({"single", "distribute"}, default = "distribute") – Default: Distribute Distribute the dataset over a file per locale. Single file will save the dataset to one file

Return type:

String message indicating result of save operation

Notes

search(pattern: bytes | arkouda.numpy.dtypes.str_scalars) → arkouda.match.Match[source]¶

Returns a match object with the first location in each element where pattern produces a match. Elements match if any part of the string matches the regular expression pattern

Parameters:: pattern (bytes or str_scalars) – Regex used to find matches
Returns:: Match object where elements match if any part of the string matches the regular expression pattern
Return type:: Match

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.search('_+')
<ak.Match object: matched=True, span=(1, 2); matched=True, span=(0, 4);
matched=False; matched=True, span=(0, 2); matched=False>

size: arkouda.numpy.dtypes.int_scalars¶

split(delimiter: str, return_segments: bool = False, regex: bool = False) → Strings | Tuple[source]¶

Unpack delimiter-joined substrings into a flat array.

Parameters:

delimiter (str) – Characters used to split strings into substrings
return_segments (bool, default=False) – If True, also return mapping of original strings to first substring in return array.
regex (bool, default=False) – Indicates whether delimiter is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

Strings – Flattened substrings with delimiters removed
pdarray, int64 (optional) – For each original string, the index of first corresponding substring in the return array

See also

peel, rpeel

Examples

>>> orig = ak.array(['one|two', 'three|four|five', 'six'])
>>> orig.split('|')
array(['one', 'two', 'three', 'four', 'five', 'six'])
>>> flat, mapping = orig.split('|', return_segments=True)
>>> mapping
array([0 2 5])
>>> under = ak.array(['one_two', 'three_____four____five', 'six'])
>>> under_split, under_map = under.split('_+', return_segments=True, regex=True)
>>> under_split
array(['one', 'two', 'three', 'four', 'five', 'six'])
>>> under_map
array([0 2 5])

startswith(substr: bytes | arkouda.numpy.dtypes.str_scalars, regex: bool = False) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Check whether each element starts with the given substring.

Parameters:

substr (bytes or str_scalars) – The prefix to search for
regex (bool, default=False) – Indicates whether substr is a regular expression Note: only handles regular expressions supported by re2 (does not support lookaheads/lookbehinds)

Returns:

True for elements that start with substr, False otherwise

Return type:

pdarray, bool

Raises:

TypeError – Raised if the substr parameter is not a bytes ior str_scalars
ValueError – Rasied if substr is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Examples

>>> strings_end = ak.array([f'string {i}' for i in range(1, 6)])
>>> strings_end
array(['string 1', 'string 2', 'string 3', 'string 4', 'string 5'])
>>> strings_end.startswith('string')
array([True True True True True])
>>> strings_start = ak.array([f'{i} string' for i in range(1,6)])
>>> strings_start
array(['1 string', '2 string', '3 string', '4 string', '5 string'])
>>> strings_start.startswith('\d str', regex = True)
array([True True True True True])

stick(other: Strings, delimiter: bytes | arkouda.numpy.dtypes.str_scalars = '', toLeft: bool = False) → Strings[source]¶

Join the strings from another array onto one end of the strings of this array, optionally inserting a delimiter. Warning: This function is experimental and not guaranteed to work.

Parameters:

other (Strings) – The strings to join onto self’s strings
delimiter (bytes or str_scalars, default="") – String inserted between self and other
toLeft (bool, default=False) – If true, join other strings to the left of self. By default, other is joined to the right of self.

Returns:

The array of joined strings

Return type:

Raises:

TypeError – Raised if the delimiter parameter is not bytes or str_scalars or if the other parameter is not a Strings instance
ValueError – Raised if times is < 1
RuntimeError – Raised if there is a server-side error thrown

See also

lstick, peel, rpeel

Examples

>>> s = ak.array(['a', 'c', 'e'])
>>> t = ak.array(['b', 'd', 'f'])
>>> s.stick(t, delimiter='.')
array(['a.b', 'c.d', 'e.f'])

strip(chars: bytes | arkouda.numpy.dtypes.str_scalars | None = '') → Strings[source]¶

Parameters:: chars (bytes or str_scalars, optional) – the set of characters to be removed
Returns:: Strings object with the leading and trailing characters matching the set of characters in the chars argument removed
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> strings = ak.array(['Strings ', '  StringS  ', 'StringS   '])
>>> s = strings.strip()
>>> s
array(['Strings', 'StringS', 'StringS'])

>>> strings = ak.array(['Strings 1', '1 StringS  ', '  1StringS  12 '])
>>> s = strings.strip(' 12')
>>> s
array(['Strings', 'StringS', 'StringS'])

sub(pattern: bytes | arkouda.numpy.dtypes.str_scalars, repl: bytes | arkouda.numpy.dtypes.str_scalars, count: int = 0) → Strings[source]¶

Return new Strings obtained by replacing non-overlapping occurrences of pattern with the replacement repl. If count is nonzero, at most count substitutions occur

Parameters:

pattern (bytes or str_scalars) – The regex to substitue
repl (bytes or str_scalars) – The substring to replace pattern matches with
count (int, default=0) – The max number of pattern match occurences in each element to replace. The default count=0 replaces all occurences of pattern with repl

Returns:

Strings with pattern matches replaced

Return type:

Raises:

TypeError – Raised if pattern or repl are not bytes or str_scalars
ValueError – Raised if pattern is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Strings.subn

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.sub(pattern='_+', repl='-', count=2)
array(['1-2-', '-', '3', '-4-5____6___7', ''])

subn(pattern: bytes | arkouda.numpy.dtypes.str_scalars, repl: bytes | arkouda.numpy.dtypes.str_scalars, count: int = 0) → Tuple[source]¶

Perform the same operation as sub(), but return a tuple (new_Strings, number_of_substitions)

Parameters:

pattern (bytes or str_scalars) – The regex to substitue
repl (bytes or str_scalars) – The substring to replace pattern matches with
count (int, default=0) – The max number of pattern match occurences in each element to replace. The default count=0 replaces all occurences of pattern with repl

Returns:

Strings – Strings with pattern matches replaced
pdarray, int64 – The number of substitutions made for each element of Strings

Raises:

TypeError – Raised if pattern or repl are not bytes or str_scalars
ValueError – Raised if pattern is not a valid regex
RuntimeError – Raised if there is a server-side error thrown

See also

Strings.sub

Examples

>>> strings = ak.array(['1_2___', '____', '3', '__4___5____6___7', ''])
>>> strings.subn(pattern='_+', repl='-', count=2)
(array(['1-2-', '-', '3', '-4-5____6___7', '']), array([2 1 0 2 0]))

title() → Strings[source]¶

Returns a new Strings from the original replaced with their titlecase equivalent.

Returns:: Strings from the original replaced with their titlecase equivalent.
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown.

See also

Strings.lower, String.upper

Examples

>>> strings = ak.array([f'StrINgS {i}' for i in range(5)])
>>> strings
array(['StrINgS 0', 'StrINgS 1', 'StrINgS 2', 'StrINgS 3', 'StrINgS 4'])
>>> strings.title()
array(['Strings 0', 'Strings 1', 'Strings 2', 'Strings 3', 'Strings 4'])

to_csv(prefix_path: str, dataset: str = 'strings_array', col_delim: str = ',', overwrite: bool = False) → str[source]¶

Parameters:

prefix_path (str) – The filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
dataset (str, default="strings_array") – Column name to save the Strings under. Defaults to “strings_array”.
col_delim (str, default=",") – Defaults to “,”. Value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
overwrite (bool, default=False) – Defaults to False. If True, any existing files matching your provided prefix_path will be overwritten. If False, an error will be returned if existing files are found.

Returns:

response message

Return type:

str

Raises:

ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server

Notes

CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for column names and data
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (\n) at this time.

Save the Strings object to HDF5. The object can be saved to a collection of files or single file.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str, default="strings_array") – The name of the Strings dataset to be written, defaults to strings_array
mode ({"truncate", "append"}, default = "truncate") – By default, truncate (overwrite) output files, if they exist. If ‘append’, create a new Strings dataset within existing files.
save_offsets (bool, default=True) – Defaults to True which will instruct the server to save the offsets array to HDF5 If False the offsets array will not be save and will be derived from the string values upon load/read.
file_type ({"single", "distribute"}, default = "distribute") – Default: Distribute Distribute the dataset over a file per locale. Single file will save the dataset to one file

Return type:

String message indicating result of save operation

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

Parquet files do not store the segments, only the values.
Strings state is saved as two datasets within an hdf5 group: one for the string characters and one for the segments corresponding to the start of each string
the hdf5 group is named via the dataset parameter.
The prefix_path must be visible to the arkouda server and the user must have write permission.
Output files have names of the form <prefix_path>_LOCALE, where  ranges from 0 to numLocales for file_type=’distribute’. Otherwise, the file name will be prefix_path.
If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, a RuntimeError will result.
Any file extension can be used.The file I/O does not rely on the extension to determine the file format.

See also

to_list() → list[source]¶

Convert the SegString to a list, transferring data from the arkouda server to Python. If the SegString exceeds a built-in size limit, a RuntimeError is raised.

Returns:: A list with the same strings as this SegString
Return type:: list

Notes

See also

Examples

>>> a = ak.array(["hello", "my", "world"])
>>> a.to_list()
['hello', 'my', 'world']
>>> type(a.to_list())
<class 'list'>

to_ndarray() → numpy.ndarray[source]¶

Convert the array to a np.ndarray, transferring array data from the arkouda server to Python. If the array exceeds a built-in size limit, a RuntimeError is raised.

Returns:: A numpy ndarray with the same strings as this array
Return type:: np.ndarray

Notes

See also

Examples

>>> a = ak.array(["hello", "my", "world"])
>>> a.to_ndarray()
array(['hello', 'my', 'world'], dtype='<U5')
>>> type(a.to_ndarray())
<class 'numpy.ndarray'>

If ‘append’, attempt to create new dataset in existing files.

Parameters:: compression ({"snappy", "gzip", "brotli", "zstd", "lz4"}, optional) – Sets the compression type used with Parquet files
Return type:: string message indicating result of save operation
Raises:: RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

The prefix_path must be visible to the arkouda server and the user must

transfer(hostname: str, port: arkouda.numpy.dtypes.int_scalars) → str | memoryview[source]¶

Sends a Strings object to a different Arkouda server

Parameters:

hostname (str) – The hostname where the Arkouda server intended to receive the Strings object is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().

Return type:

A message indicating a complete transfer

Raises:

ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype

unregister() → None[source]¶

Unregister a Strings object in the arkouda server which was previously registered using register() and/or attached to using attach()

Return type:: None
Raises:: RuntimeError – Raised if the server could not find the internal name/symbol to remove

See also

register, attach

Notes

Registered names/Strings objects in the server are immune to deletion until they are unregistered.

static unregister_strings_by_name(user_defined_name: str) → None[source]¶

Unregister a Strings object in the arkouda server previously registered via register()

Parameters:: user_defined_name (str) – The registered name of the Strings object

update_hdf(prefix_path: str, dataset: str = 'strings_array', save_offsets: bool = True, repack: bool = True) → str[source]¶

Overwrite the dataset with the name provided with this Strings object. If the dataset does not exist it is added

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str, default="strings_array") – Name of the dataset to create in files
save_offsets (bool, default=True) – Defaults to True which will instruct the server to save the offsets array to HDF5 If False the offsets array will not be save and will be derived from the string values upon load/read.
repack (bool, default=True) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.

Return type:

str - success message if successful

Raises:

RuntimeError – Raised if a server-side error is thrown saving the Strings object

Notes

If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added

upper() → Strings[source]¶

Returns a new Strings with all lowercase characters from the original replaced with their uppercase equivalent

Returns:: Strings with all lowercase characters from the original replaced with their uppercase equivalent
Return type:: Strings
Raises:: RuntimeError – Raised if there is a server-side error thrown

See also

Strings.lower

Examples

>>> strings = ak.array([f'StrINgS {i}' for i in range(5)])
>>> strings
array(['StrINgS 0', 'StrINgS 1', 'StrINgS 2', 'StrINgS 3', 'StrINgS 4'])
>>> strings.upper()
array(['STRINGS 0', 'STRINGS 1', 'STRINGS 2', 'STRINGS 3', 'STRINGS 4'])

class arkouda.Timedelta(pda, unit: str = _BASE_UNIT)[source]¶

Bases: _AbstractBaseTime

Represents a duration, the difference between two dates or times.

Timedelta is the Arkouda equivalent of pandas.TimedeltaIndex.

Parameters:

pda (int64 pdarray, pd.TimedeltaIndex, pd.Series, or np.timedelta64 array)
unit (str, default 'ns') –
For int64 pdarray, denotes the unit of the input. Ignored for pandas and numpy arrays, which carry their own unit. Not case-sensitive; prefixes of full names (like ‘sec’) are accepted.

Possible values:
- ’weeks’ or ‘w’
- ’days’ or ‘d’
- ’hours’ or ‘h’
- ’minutes’, ‘m’, or ‘t’
- ’seconds’ or ‘s’
- ’milliseconds’, ‘ms’, or ‘l’
- ’microseconds’, ‘us’, or ‘u’
- ’nanoseconds’, ‘ns’, or ‘n’
Unlike in pandas, units cannot be combined or mixed with integers

Notes

The .values attribute is always in nanoseconds with int64 dtype.

abs()[source]¶: Absolute value of time interval.

property components¶

property days¶

is_registered() → numpy.bool_[source]¶

Return True iff the object is contained in the registry or is a component of a registered object.

Returns:: Indicates if the object is contained in the registry
Return type:: numpy.bool
Raises:: RegistrationError – Raised if there’s a server-side error or a mis-match of registered components

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

property microseconds¶

property nanoseconds¶

register(user_defined_name)[source]¶

Parameters:

user_defined_name (str) – user defined name the timedelta is to be registered under, this will be the root name for underlying components

Returns:

The same Timedelta which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different Timedeltas with the same name.

Return type:

Timedelta

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the timedelta with the user_defined_name

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

property seconds¶

special_objType = 'Timedelta'¶

std(ddof: arkouda.numpy.dtypes.int_scalars = 0)[source]¶: Returns the standard deviation as a pd.Timedelta object

sum()[source]¶

Return sum of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numpy_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numpy_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.sum(ak.array([1,2,3,4,5]))
15
>>> ak.sum(ak.array([5.5,4.5,3.5,2.5,1.5]))
17.5
>>> ak.array([[1,2,3],[5,4,3]]).sum(axis=1)
array([6 12])

Notes

Works as a method of a pdarray (e.g. a.sum()) or a standalone function (e.g. ak.sum(a))

supported_opeq¶

supported_with_datetime¶

supported_with_pdarray¶

supported_with_r_datetime¶

supported_with_r_pdarray¶

supported_with_r_timedelta¶

supported_with_timedelta¶

to_pandas()[source]¶: Convert array to a pandas TimedeltaIndex. Note: if the array size exceeds client.maxTransferBytes, a RuntimeError is raised.

See also

to_ndarray

total_seconds()[source]¶

unregister()[source]¶

Unregister this timedelta object in the arkouda server which was previously registered using register() and/or attached to using attach()

Raises:: RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

class arkouda.Timedelta(pda, unit: str = _BASE_UNIT)[source]¶

Bases: _AbstractBaseTime

Represents a duration, the difference between two dates or times.

Timedelta is the Arkouda equivalent of pandas.TimedeltaIndex.

Parameters:

pda (int64 pdarray, pd.TimedeltaIndex, pd.Series, or np.timedelta64 array)
unit (str, default 'ns') –
For int64 pdarray, denotes the unit of the input. Ignored for pandas and numpy arrays, which carry their own unit. Not case-sensitive; prefixes of full names (like ‘sec’) are accepted.

Possible values:
- ’weeks’ or ‘w’
- ’days’ or ‘d’
- ’hours’ or ‘h’
- ’minutes’, ‘m’, or ‘t’
- ’seconds’ or ‘s’
- ’milliseconds’, ‘ms’, or ‘l’
- ’microseconds’, ‘us’, or ‘u’
- ’nanoseconds’, ‘ns’, or ‘n’
Unlike in pandas, units cannot be combined or mixed with integers

Notes

The .values attribute is always in nanoseconds with int64 dtype.

abs()[source]¶: Absolute value of time interval.

property components¶

property days¶

is_registered() → numpy.bool_[source]¶

Return True iff the object is contained in the registry or is a component of a registered object.

Returns:: Indicates if the object is contained in the registry
Return type:: numpy.bool
Raises:: RegistrationError – Raised if there’s a server-side error or a mis-match of registered components

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

property microseconds¶

property nanoseconds¶

register(user_defined_name)[source]¶

Parameters:

user_defined_name (str) – user defined name the timedelta is to be registered under, this will be the root name for underlying components

Returns:

Return type:

Timedelta

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the timedelta with the user_defined_name

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

property seconds¶

special_objType = 'Timedelta'¶

std(ddof: arkouda.numpy.dtypes.int_scalars = 0)[source]¶: Returns the standard deviation as a pd.Timedelta object

sum()[source]¶

Return sum of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numpy_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numpy_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.sum(ak.array([1,2,3,4,5]))
15
>>> ak.sum(ak.array([5.5,4.5,3.5,2.5,1.5]))
17.5
>>> ak.array([[1,2,3],[5,4,3]]).sum(axis=1)
array([6 12])

Notes

Works as a method of a pdarray (e.g. a.sum()) or a standalone function (e.g. ak.sum(a))

supported_opeq¶

supported_with_datetime¶

supported_with_pdarray¶

supported_with_r_datetime¶

supported_with_r_pdarray¶

supported_with_r_timedelta¶

supported_with_timedelta¶

to_pandas()[source]¶: Convert array to a pandas TimedeltaIndex. Note: if the array size exceeds client.maxTransferBytes, a RuntimeError is raised.

See also

to_ndarray

total_seconds()[source]¶

unregister()[source]¶

Unregister this timedelta object in the arkouda server which was previously registered using register() and/or attached to using attach()

Raises:: RegistrationError – If the object is already unregistered or if there is a server error when attempting to unregister

See also

Notes

Objects registered with the server are immune to deletion until they are unregistered.

class arkouda.Tuple(origin, nparams, *, inst=True, name=None)¶

Bases: _SpecialGenericAlias

Tuple type; Tuple[X, Y] is the cross-product type of X and Y.

Example: Tuple[T1, T2] is a tuple of two elements corresponding to type variables T1 and T2. Tuple[int, float, str] is a tuple of an int, a float and a string.

To specify a variable-length tuple of homogeneous type, use Tuple[T, …].

class arkouda.Union[source]¶

Bases: _Final

Union type; Union[X, Y] means either X or Y.

To define a union, use e.g. Union[int, str]. Details: - The arguments must be types and there must be at least one. - None as an argument is a special case and is replaced by

type(None).
Unions of unions are flattened, e.g.:
Union[Union[int, str], float] == Union[int, str, float]
Unions of a single argument vanish, e.g.:
Union[int] == int  # The constructor actually returns int
Redundant arguments are skipped, e.g.:
Union[int, str, int] == Union[int, str]
When comparing unions, the argument order is ignored, e.g.:
Union[int, str] == Union[str, int]
You cannot subclass or instantiate a union.

You can use Optional[X] as a shorthand for Union[X, None].

arkouda.VAL_SUFFIX = '_values'¶

Convert a Python or Numpy Iterable to a pdarray or Strings object, sending the corresponding data to the arkouda server.

Parameters:

a (Union[pdarray, np.ndarray]) – array of a supported dtype
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays

Returns:

A pdarray instance stored on arkouda server or Strings instance, which is composed of two pdarrays stored on arkouda server

Return type:

Raises:

TypeError – Raised if a is not a pdarray, np.ndarray, or Python Iterable such as a list, array, tuple, or deque
RuntimeError – Raised if nbytes > maxTransferBytes, a.dtype is not supported (not in DTypes), or if the product of a size and a.itemsize > maxTransferBytes
ValueError – Raised if a has rank is not in get_array_ranks(), or if the returned message is malformed or does not contain the fields required to generate the array.

See also

Notes

The number of bytes in the input array cannot exceed ak.client.maxTransferBytes, otherwise a RuntimeError will be raised. This is to protect the user from overwhelming the connection between the Python client and the arkouda server, under the assumption that it is a low-bandwidth connection. The user may override this limit by setting ak.client.maxTransferBytes to a larger value, but should proceed with caution.

If the pdrray or ndarray is of type U, this method is called twice recursively to create the Strings object and the two corresponding pdarrays for string bytes and offsets, respectively.

Examples

>>> ak.array(np.arange(1,10))
array([1 2 3 4 5 6 7 8 9])

>>> ak.array(range(1,10))
array([1 2 3 4 5 6 7 8 9])

>>> strings = ak.array([f'string {i}' for i in range(0,5)])
>>> type(strings)
<class 'arkouda.numpy.strings.Strings'>

arkouda.akcast(typ, val)¶: Cast a value to a type.

This returns the value unchanged. To the type checker this signals that the return value has the designated type, but at runtime we intentionally don’t check anything (we want this to be as fast as possible).

arkouda.all_scalars¶: The DType enum defines the supported Arkouda data types in string form.

arkouda.apply(arr: arkouda.numpy.pdarrayclass.pdarray, func: Callable | str, result_dtype: numpy.dtype | str | None = None) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Apply a python function to a pdarray. The function should take one argument and return a new value. The function will then be called on each element in the pdarray.

For example, >>> ak.apply(ak.array([1, 2, 3]), lambda x: x+1)

Or, >>> import math >>> arr = ak.randint(0, 10, 10_000) >>> def times_pi(x):

return x*math.pi

>>> ak.apply(arr, times_pi, "float64")

Warning: This function is experimental and may not work as expected. Known limitations: - Any python modules used inside of the function must be installed on the server.

Parameters:

arr (pdarray) – The pdarray to which the function is applied
func (Union[Callable, str]) – The function to apply to the array. This can be a callable function or a string, but either way it should take a single argument and return a single value. If a string, it should be a lambda function that takes a single argument, e.g. “lambda x,: x+1”. Note the dangling comma after the argument, this is required for string functions.
result_dtype (Optional[Union[np.dtype, str]]) – The dtype of the resulting pdarray. If None, the dtype of the resulting pdarray will be the same as the input pdarray. If a string, it should be a valid numpy dtype string, e.g. “float64”. If a numpy dtype, it should be a valid numpy dtype object, e.g. np.float64. This is not supported for functions passed as strings.

Returns:

The pdarray resulting from applying the function to the input array

Return type:

arkouda.arange(*args, **kwargs) → arkouda.numpy.pdarrayclass.pdarray[source]¶

arange([start,] stop[, stride,] dtype=int64)

Create a pdarray of consecutive integers within the interval [start, stop). If only one arg is given then arg is the stop parameter. If two args are given, then the first arg is start and second is stop. If three args are given, then the first arg is start, second is stop, third is stride.

The return value is cast to type dtype

Parameters:

start (int_scalars, optional) – Starting value (inclusive)
stop (int_scalars) – Stopping value (exclusive)
stride (int_scalars, optional) – The difference between consecutive elements, the default stride is 1, if stride is specified then start must also be specified.
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays

Returns:

Integers from start (inclusive) to stop (exclusive) by stride

Return type:

pdarray, dtype

Raises:

TypeError – Raised if start, stop, or stride is not an int object
ZeroDivisionError – Raised if stride == 0

See also

Notes

Negative strides result in decreasing values. Currently, only int64 pdarrays can be created with this method. For float64 arrays, use the linspace method.

Examples

>>> ak.arange(0, 5, 1)
array([0 1 2 3 4])

>>> ak.arange(5, 0, -1)
array([5 4 3 2 1])

>>> ak.arange(0, 10, 2)
array([0 2 4 6 8])

>>> ak.arange(-5, -10, -1)
array([-5 -6 -7 -8 -9])

arkouda.arange(*args, **kwargs) → arkouda.numpy.pdarrayclass.pdarray[source]¶

arange([start,] stop[, stride,] dtype=int64)

The return value is cast to type dtype

Parameters:

start (int_scalars, optional) – Starting value (inclusive)
stop (int_scalars) – Stopping value (exclusive)
stride (int_scalars, optional) – The difference between consecutive elements, the default stride is 1, if stride is specified then start must also be specified.
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays

Returns:

Integers from start (inclusive) to stop (exclusive) by stride

Return type:

pdarray, dtype

Raises:

TypeError – Raised if start, stop, or stride is not an int object
ZeroDivisionError – Raised if stride == 0

See also

Notes

Negative strides result in decreasing values. Currently, only int64 pdarrays can be created with this method. For float64 arrays, use the linspace method.

Examples

>>> ak.arange(0, 5, 1)
array([0 1 2 3 4])

>>> ak.arange(5, 0, -1)
array([5 4 3 2 1])

>>> ak.arange(0, 10, 2)
array([0 2 4 6 8])

>>> ak.arange(-5, -10, -1)
array([-5 -6 -7 -8 -9])

arkouda.arange(*args, **kwargs) → arkouda.numpy.pdarrayclass.pdarray[source]¶

arange([start,] stop[, stride,] dtype=int64)

The return value is cast to type dtype

Parameters:

start (int_scalars, optional) – Starting value (inclusive)
stop (int_scalars) – Stopping value (exclusive)
stride (int_scalars, optional) – The difference between consecutive elements, the default stride is 1, if stride is specified then start must also be specified.
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays

Returns:

Integers from start (inclusive) to stop (exclusive) by stride

Return type:

pdarray, dtype

Raises:

TypeError – Raised if start, stop, or stride is not an int object
ZeroDivisionError – Raised if stride == 0

See also

Notes

Negative strides result in decreasing values. Currently, only int64 pdarrays can be created with this method. For float64 arrays, use the linspace method.

Examples

>>> ak.arange(0, 5, 1)
array([0 1 2 3 4])

>>> ak.arange(5, 0, -1)
array([5 4 3 2 1])

>>> ak.arange(0, 10, 2)
array([0 2 4 6 8])

>>> ak.arange(-5, -10, -1)
array([-5 -6 -7 -8 -9])

arkouda.arange(*args, **kwargs) → arkouda.numpy.pdarrayclass.pdarray[source]¶

arange([start,] stop[, stride,] dtype=int64)

The return value is cast to type dtype

Parameters:

start (int_scalars, optional) – Starting value (inclusive)
stop (int_scalars) – Stopping value (exclusive)
stride (int_scalars, optional) – The difference between consecutive elements, the default stride is 1, if stride is specified then start must also be specified.
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays

Returns:

Integers from start (inclusive) to stop (exclusive) by stride

Return type:

pdarray, dtype

Raises:

TypeError – Raised if start, stop, or stride is not an int object
ZeroDivisionError – Raised if stride == 0

See also

Notes

Negative strides result in decreasing values. Currently, only int64 pdarrays can be created with this method. For float64 arrays, use the linspace method.

Examples

>>> ak.arange(0, 5, 1)
array([0 1 2 3 4])

>>> ak.arange(5, 0, -1)
array([5 4 3 2 1])

>>> ak.arange(0, 10, 2)
array([0 2 4 6 8])

>>> ak.arange(-5, -10, -1)
array([-5 -6 -7 -8 -9])

arkouda.argmaxk(pda: pdarray, k: arkouda.numpy.dtypes.int_scalars) → pdarray[source]¶

Find the indices corresponding to the k maximum values of an array.

Returns the largest k values of an array, sorted

Parameters:

pda (pdarray) – Input array.
k (int_scalars) – The desired count of indices corresponding to maxmum array values

Returns:

The indices of the maximum k values from the pda, sorted

Return type:

pdarray, int

Raises:

TypeError – Raised if pda is not a pdarray or k is not an integer
ValueError – Raised if the pda is empty, or pda.ndim > 1, or k < 1

Notes

This call is equivalent in value to ak.argsort(a)[k:] and generally outperforms this operation.

This reduction will see a significant drop in performance as k grows beyond a certain value. This value is system dependent, but generally about a k of 5 million is where performance degradation has been observed.

Examples

>>> A = ak.array([10,5,1,3,7,2,9,0])
>>> ak.argmaxk(A, 3)
array([4, 6, 0])
>>> ak.argmaxk(A, 4)
array([1, 4, 6, 0])

arkouda.argmink(pda: pdarray, k: arkouda.numpy.dtypes.int_scalars) → pdarray[source]¶

Finds the indices corresponding to the k minimum values of an array.

Parameters:

pda (pdarray) – Input array.
k (int_scalars) – The desired count of indices corresponding to minimum array values

Returns:

The indices of the minimum k values from the pda, sorted

Return type:

pdarray, int

Raises:

TypeError – Raised if pda is not a pdarray or k is not an integer
ValueError – Raised if the pda is empty, or pda.ndim > 1, or k < 1

Notes

This call is equivalent in value to ak.argsort(a)[:k] and generally outperforms this operation.

Examples

>>> A = ak.array([10,5,1,3,7,2,9,0])
>>> ak.argmink(A, 3)
array([7, 2, 5])
>>> ak.argmink(A, 4)
array([7, 2, 5, 3])

arkouda.argsort(pda: arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.categorical.Categorical, algorithm: SortingAlgorithm = SortingAlgorithm.RadixSortLSD, axis: arkouda.numpy.dtypes.int_scalars = 0) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the permutation that sorts the array.

Parameters:

pda (pdarray, Strings, or Categorical) – The array to sort (int64, uint64, or float64)
algorithm (SortingAlgorithm, default=SortingAlgorithm.RadixSortLSD) – The algorithm to be used for sorting the array.
axis (int_scalars, default=0) – The axis to sort over.

Returns:

The indices such that pda[indices] is sorted

Return type:

pdarray of int64

Raises:

TypeError – Raised if the parameter is other than a pdarray, Strings or Categorical

See also

Notes

Uses a least-significant-digit radix sort, which is stable and resilient to non-uniformity in data but communication intensive.

Examples

>>> a = ak.randint(0, 10, 10)
>>> perm = ak.argsort(a)
>>> a[perm]
array([0 1 3 3 5 5 5 6 6 6])

>>> ak.argsort(a, ak.sorting.SortingAlgorithm["RadixSortLSD"])
array([0 2 9 6 8 1 3 5 7 4])

>>> ak.argsort(a, ak.sorting.SortingAlgorithm["TwoArrayRadixSort"])
array([0 2 9 6 8 1 3 5 7 4])

Return the permutation that sorts the array.

Parameters:

pda (pdarray, Strings, or Categorical) – The array to sort (int64, uint64, or float64)
algorithm (SortingAlgorithm, default=SortingAlgorithm.RadixSortLSD) – The algorithm to be used for sorting the array.
axis (int_scalars, default=0) – The axis to sort over.

Returns:

The indices such that pda[indices] is sorted

Return type:

pdarray of int64

Raises:

TypeError – Raised if the parameter is other than a pdarray, Strings or Categorical

See also

Notes

Uses a least-significant-digit radix sort, which is stable and resilient to non-uniformity in data but communication intensive.

Examples

>>> a = ak.randint(0, 10, 10)
>>> perm = ak.argsort(a)
>>> a[perm]
array([0 1 3 3 5 5 5 6 6 6])

>>> ak.argsort(a, ak.sorting.SortingAlgorithm["RadixSortLSD"])
array([0 2 9 6 8 1 3 5 7 4])

>>> ak.argsort(a, ak.sorting.SortingAlgorithm["TwoArrayRadixSort"])
array([0 2 9 6 8 1 3 5 7 4])

Convert a Python or Numpy Iterable to a pdarray or Strings object, sending the corresponding data to the arkouda server.

Parameters:

a (Union[pdarray, np.ndarray]) – array of a supported dtype
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays

Returns:

A pdarray instance stored on arkouda server or Strings instance, which is composed of two pdarrays stored on arkouda server

Return type:

Raises:

TypeError – Raised if a is not a pdarray, np.ndarray, or Python Iterable such as a list, array, tuple, or deque
RuntimeError – Raised if nbytes > maxTransferBytes, a.dtype is not supported (not in DTypes), or if the product of a size and a.itemsize > maxTransferBytes
ValueError – Raised if a has rank is not in get_array_ranks(), or if the returned message is malformed or does not contain the fields required to generate the array.

See also

Notes

If the pdrray or ndarray is of type U, this method is called twice recursively to create the Strings object and the two corresponding pdarrays for string bytes and offsets, respectively.

Examples

>>> ak.array(np.arange(1,10))
array([1 2 3 4 5 6 7 8 9])

>>> ak.array(range(1,10))
array([1 2 3 4 5 6 7 8 9])

>>> strings = ak.array([f'string {i}' for i in range(0,5)])
>>> type(strings)
<class 'arkouda.numpy.strings.Strings'>

Convert a Python or Numpy Iterable to a pdarray or Strings object, sending the corresponding data to the arkouda server.

Parameters:

a (Union[pdarray, np.ndarray]) – array of a supported dtype
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays

Returns:

A pdarray instance stored on arkouda server or Strings instance, which is composed of two pdarrays stored on arkouda server

Return type:

Raises:

TypeError – Raised if a is not a pdarray, np.ndarray, or Python Iterable such as a list, array, tuple, or deque
RuntimeError – Raised if nbytes > maxTransferBytes, a.dtype is not supported (not in DTypes), or if the product of a size and a.itemsize > maxTransferBytes
ValueError – Raised if a has rank is not in get_array_ranks(), or if the returned message is malformed or does not contain the fields required to generate the array.

See also

Notes

If the pdrray or ndarray is of type U, this method is called twice recursively to create the Strings object and the two corresponding pdarrays for string bytes and offsets, respectively.

Examples

>>> ak.array(np.arange(1,10))
array([1 2 3 4 5 6 7 8 9])

>>> ak.array(range(1,10))
array([1 2 3 4 5 6 7 8 9])

>>> strings = ak.array([f'string {i}' for i in range(0,5)])
>>> type(strings)
<class 'arkouda.numpy.strings.Strings'>

Convert a Python or Numpy Iterable to a pdarray or Strings object, sending the corresponding data to the arkouda server.

Parameters:

a (Union[pdarray, np.ndarray]) – array of a supported dtype
dtype (np.dtype, type, or str) – The target dtype to cast values to
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays

Returns:

A pdarray instance stored on arkouda server or Strings instance, which is composed of two pdarrays stored on arkouda server

Return type:

Raises:

TypeError – Raised if a is not a pdarray, np.ndarray, or Python Iterable such as a list, array, tuple, or deque
RuntimeError – Raised if nbytes > maxTransferBytes, a.dtype is not supported (not in DTypes), or if the product of a size and a.itemsize > maxTransferBytes
ValueError – Raised if a has rank is not in get_array_ranks(), or if the returned message is malformed or does not contain the fields required to generate the array.

See also

assert_arkouda_array_equal

Notes

If the pdrray or ndarray is of type U, this method is called twice recursively to create the Strings object and the two corresponding pdarrays for string bytes and offsets, respectively.

Examples

>>> ak.array(np.arange(1,10))
array([1 2 3 4 5 6 7 8 9])

>>> ak.array(range(1,10))
array([1 2 3 4 5 6 7 8 9])

>>> strings = ak.array([f'string {i}' for i in range(0,5)])
>>> type(strings)
<class 'arkouda.numpy.strings.Strings'>

arkouda.assert_almost_equal(left, right, rtol: float = 1e-05, atol: float = 1e-08, **kwargs) → None[source]¶

Check that the left and right objects are approximately equal.

By approximately equal, we refer to objects that are numbers or that contain numbers which may be equivalent to specific levels of precision.

Parameters:

left (object)
right (object)
rtol (float, default 1e-5) – Relative tolerance.
atol (float, default 1e-8) – Absolute tolerance.

Warning

This function cannot be used on pdarray of size > ak.client.maxTransferBytes because it converts pdarrays to numpy arrays and calls np.allclose.

arkouda.assert_almost_equivalent(left, right, rtol: float = 1e-05, atol: float = 1e-08) → None[source]¶

Check that the left and right objects are approximately equal.

By approximately equal, we refer to objects that are numbers or that contain numbers which may be equivalent to specific levels of precision.

If the objects are pandas or numpy objects, they are converted to arkouda objects. Then assert_almost_equal is applied to the result.

Parameters:

left (object)
right (object)
rtol (float, default 1e-5) – Relative tolerance.
atol (float, default 1e-8) – Absolute tolerance.

Warning

This function cannot be used on pdarray of size > ak.client.maxTransferBytes because it converts pdarrays to numpy arrays and calls np.allclose.

See also

assert_almost_equal

arkouda.assert_arkouda_array_equal(left: arkouda.pdarray | arkouda.Strings | arkouda.Categorical | arkouda.SegArray, right: arkouda.pdarray | arkouda.Strings | arkouda.Categorical | arkouda.SegArray, check_dtype: bool = True, err_msg=None, check_same=None, obj: str = 'pdarray', index_values=None) → None[source]¶

Check that ‘ak.pdarray’ or ‘ak.Strings’, ‘ak.Categorical’, or ‘ak.SegArray’ is equivalent.

Parameters:

left (arkouda.pdarray or arkouda.numpy.Strings or arkouda.Categorical or)
right (arkouda.pdarray or arkouda.numpy.Strings or arkouda.Categorical or)
arkouda.numpy.SegArray – The two arrays to be compared.
check_dtype (bool, default True) – Check dtype if both a and b are ak.pdarray.
err_msg (str, default None) – If provided, used as assertion message.
check_same (None|'copy'|'same', default None) – Ensure left and right refer/do not refer to the same memory area.
obj (str, default 'numpy array') – Specify object name being compared, internally used to show appropriate assertion message.
index_values (Index | arkouda.pdarray, default None) – optional index (shared by both left and right), used in output.

Check that ‘np.array’, ‘pd.Categorical’, ‘ak.pdarray’, ‘ak.Strings’, ‘ak.Categorical’, or ‘ak.SegArray’ is equivalent.

np.nparray’s and pd.Categorical’s will be converted to the arkouda equivalent. Then assert_arkouda_pdarray_equal will be applied to the result.

Parameters:

left (np.ndarray, pd.Categorical, arkouda.pdarray or arkouda.numpy.Strings or)
right (np.ndarray, pd.Categorical, arkouda.pdarray or arkouda.numpy.Strings or)
arkouda.Categorical – The two arrays to be compared.
check_dtype (bool, default True) – Check dtype if both a and b are ak.pdarray or np.ndarray.
err_msg (str, default None) – If provided, used as assertion message.
check_same (None|'copy'|'same', default None) – Ensure left and right refer/do not refer to the same memory area.
obj (str, default 'numpy array') – Specify object name being compared, internally used to show appropriate assertion message.
index_values (Index | arkouda.pdarray, default None) – optional index (shared by both left and right), used in output.

See also

arkouda.assert_arkouda_pdarray_equal(left: arkouda.pdarray, right: arkouda.pdarray, check_dtype: bool = True, err_msg=None, check_same=None, obj: str = 'pdarray', index_values=None) → None[source]¶

Check that the two ‘ak.pdarray’s are equivalent.

Parameters:

left (arkouda.pdarray) – The two arrays to be compared.
right (arkouda.pdarray) – The two arrays to be compared.
check_dtype (bool, default True) – Check dtype if both a and b are ak.pdarray.
err_msg (str, default None) – If provided, used as assertion message.
check_same (None|'copy'|'same', default None) – Ensure left and right refer/do not refer to the same memory area.
obj (str, default 'pdarray') – Specify object name being compared, internally used to show appropriate assertion message.
index_values (Index | arkouda.pdarray, default None) – optional index (shared by both left and right), used in output.

arkouda.assert_arkouda_segarray_equal(left: arkouda.SegArray, right: arkouda.SegArray, check_dtype: bool = True, err_msg=None, check_same=None, obj: str = 'segarray') → None[source]¶

Check that the two ‘ak.segarray’s are equivalent.

Parameters:

left (arkouda.numpy.SegArray) – The two segarrays to be compared.
right (arkouda.numpy.SegArray) – The two segarrays to be compared.
check_dtype (bool, default True) – Check dtype if both a and b are ak.pdarray.
err_msg (str, default None) – If provided, used as assertion message.
check_same (None|'copy'|'same', default None) – Ensure left and right refer/do not refer to the same memory area.
obj (str, default 'pdarray') – Specify object name being compared, internally used to show appropriate assertion message.

arkouda.assert_arkouda_strings_equal(left, right, err_msg=None, check_same=None, obj: str = 'Strings', index_values=None) → None[source]¶

Check that ‘ak.Strings’ is equivalent.

Parameters:

left (arkouda.numpy.Strings) – The two Strings to be compared.
right (arkouda.numpy.Strings) – The two Strings to be compared.
err_msg (str, default None) – If provided, used as assertion message.
check_same (None|'copy'|'same', default None) – Ensure left and right refer/do not refer to the same memory area.
obj (str, default 'Strings') – Specify object name being compared, internally used to show appropriate assertion message.
index_values (Index | arkouda.pdarray, default None) – optional index (shared by both left and right), used in output.

arkouda.assert_attr_equal(attr: str, left, right, obj: str = 'Attributes') → None[source]¶

Check attributes are equal. Both objects must have attribute.

Parameters:

attr (str) – Attribute name being compared.
left (object)
right (object)
obj (str, default 'Attributes') – Specify object name being compared, internally used to show appropriate assertion message

arkouda.assert_categorical_equal(left, right, check_dtype: bool = True, check_category_order: bool = True, obj: str = 'Categorical') → None[source]¶

Test that Categoricals are equivalent.

Parameters:

left (Categorical)
right (Categorical)
check_dtype (bool, default True) – Check that integer dtype of the codes are the same.
check_category_order (bool, default True) – Whether the order of the categories should be compared, which implies identical integer codes. If False, only the resulting values are compared. The ordered attribute is checked regardless.
obj (str, default 'Categorical') – Specify object name being compared, internally used to show appropriate assertion message.

arkouda.assert_class_equal(left, right, exact: bool = True, obj: str = 'Input') → None[source]¶: Checks classes are equal.

arkouda.assert_contains_all(iterable, dic) → None[source]¶: Assert that a dictionary contains all the elements of an iterable. :param iterable: :type iterable: iterable :param dic: :type dic: dict

arkouda.assert_copy(iter1, iter2, **eql_kwargs) → None[source]¶

Checks that the elements are equal, but not the same object. (Does not check that items in sequences are also not the same object.)

Parameters:

iter1 (iterable) – Iterables that produce elements comparable with assert_almost_equal.
iter2 (iterable) – Iterables that produce elements comparable with assert_almost_equal.

arkouda.assert_dict_equal(left, right, compare_keys: bool = True) → None[source]¶

Assert that two dictionaries are equal. Values must be arkouda objects. :param left: The dictionaries to be compared. :type left: dict :param right: The dictionaries to be compared. :type right: dict :param compare_keys: Whether to compare the keys.

If False, only the values are compared.

arkouda.assert_equal(left, right, **kwargs) → None[source]¶

Wrapper for tm.assert_*_equal to dispatch to the appropriate test function.

Parameters:

left (Index, Series, DataFrame, or pdarray) – The two items to be compared.
right (Index, Series, DataFrame, or pdarray) – The two items to be compared.
**kwargs – All keyword arguments are passed through to the underlying assert method.

arkouda.assert_equivalent(left, right, **kwargs) → None[source]¶

Wrapper for tm.assert_*_equivalent to dispatch to the appropriate test function.

Parameters:

left (Index, pd.Index, Series, pd.Series, DataFrame, pd.DataFrame,)
right (Index, pd.Index, Series, pd.Series, DataFrame, pd.DataFrame,)
Strings – The two items to be compared.
Categorical – The two items to be compared.
pd.Categorical – The two items to be compared.
SegArray – The two items to be compared.
pdarray – The two items to be compared.
np.ndarray – The two items to be compared.

:param : The two items to be compared. :param **kwargs: All keyword arguments are passed through to the underlying assert method.

arkouda.assert_frame_equal(left: arkouda.DataFrame, right: arkouda.DataFrame, check_dtype: bool = True, check_index_type: bool = True, check_column_type: bool = True, check_frame_type: bool = True, check_names: bool = True, check_exact: bool = True, check_categorical: bool = True, check_like: bool = False, rtol: float = 1e-05, atol: float = 1e-08, obj: str = 'DataFrame') → None[source]¶

Check that left and right DataFrame are equal.

This function is intended to compare two DataFrames and output any differences. It is mostly intended for use in unit tests. Additional parameters allow varying the strictness of the equality checks performed.

Parameters:

left (DataFrame) – First DataFrame to compare.
right (DataFrame) – Second DataFrame to compare.
check_dtype (bool, default True) – Whether to check the DataFrame dtype is identical.
check_index_type (bool, default = True) – Whether to check the Index class, dtype and inferred_type are identical.
check_column_type (bool or {'equiv'}, default 'equiv') – Whether to check the columns class, dtype and inferred_type are identical. Is passed as the exact argument of assert_index_equal().
check_frame_type (bool, default True) – Whether to check the DataFrame class is identical.
check_names (bool, default True) – Whether to check that the names attribute for both the index and column attributes of the DataFrame is identical.
check_exact (bool, default False) – Whether to compare number exactly.
check_categorical (bool, default True) – Whether to compare internal Categorical exactly.
check_like (bool, default False) – If True, ignore the order of index & columns. Note: index labels must match their respective rows (same as in columns) - same labels must be with the same data.
rtol (float, default 1e-5) – Relative tolerance. Only used when check_exact is False.
atol (float, default 1e-8) – Absolute tolerance. Only used when check_exact is False.
obj (str, default 'DataFrame') – Specify object name being compared, internally used to show appropriate assertion message.

See also

assert_series_equal: Equivalent method for asserting Series equality.

Examples

This example shows comparing two DataFrames that are equal but with columns of differing dtypes.

>>> from arkouda.testing import assert_frame_equal
>>> df1 = ak.DataFrame({'a': [1, 2], 'b': [3, 4]})
>>> df2 = ak.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]})

df1 equals itself.

>>> assert_frame_equal(df1, df1)

df1 differs from df2 as column ‘b’ is of a different type.

>>> assert_frame_equal(df1, df2)
Traceback (most recent call last):
...
AssertionError: Attributes of DataFrame.iloc[:, 1] (column name="b") are different

Attribute “dtype” are different [left]: int64 [right]: float64

Ignore differing dtypes in columns with check_dtype.

>>> assert_frame_equal(df1, df2, check_dtype=False)

arkouda.assert_frame_equivalent(left: arkouda.DataFrame | pandas.DataFrame, right: arkouda.DataFrame | pandas.DataFrame, check_dtype: bool = True, check_index_type: bool = True, check_column_type: bool = True, check_frame_type: bool = True, check_names: bool = True, check_exact: bool = True, check_categorical: bool = True, check_like: bool = False, rtol: float = 1e-05, atol: float = 1e-08, obj: str = 'DataFrame') → None[source]¶

Check that left and right DataFrame are equal.

pd.DataFrame’s will be converted to the arkouda equivalent. Then assert_frame_equal will be applied to the result.

Parameters:

left (DataFrame or pd.DataFrame) – First DataFrame to compare.
right (DataFrame or pd.DataFrame) – Second DataFrame to compare.
check_dtype (bool, default True) – Whether to check the DataFrame dtype is identical.
check_index_type (bool, default = True) – Whether to check the Index class, dtype and inferred_type are identical.
check_column_type (bool or {'equiv'}, default 'equiv') – Whether to check the columns class, dtype and inferred_type are identical. Is passed as the exact argument of assert_index_equal().
check_frame_type (bool, default True) – Whether to check the DataFrame class is identical.
check_names (bool, default True) – Whether to check that the names attribute for both the index and column attributes of the DataFrame is identical.
check_exact (bool, default False) – Whether to compare number exactly.
check_categorical (bool, default True) – Whether to compare internal Categorical exactly.
check_like (bool, default False) – If True, ignore the order of index & columns. Note: index labels must match their respective rows (same as in columns) - same labels must be with the same data.
rtol (float, default 1e-5) – Relative tolerance. Only used when check_exact is False.
atol (float, default 1e-8) – Absolute tolerance. Only used when check_exact is False.
obj (str, default 'DataFrame') – Specify object name being compared, internally used to show appropriate assertion message.

See also

assert_frame_equal

Examples

This example shows comparing two DataFrames that are equal but with columns of differing dtypes.

>>> from arkouda.testing import assert_frame_equivalent
>>> import pandas as pd
>>> df1 = ak.DataFrame({'a': [1, 2], 'b': [3, 4]})
>>> df2 = pd.DataFrame({'a': [1, 2], 'b': [3.0, 4.0]})
>>> assert_frame_equivalent(df1, df1)

arkouda.assert_index_equal(left: arkouda.Index, right: arkouda.Index, exact: bool = True, check_names: bool = True, check_exact: bool = True, check_categorical: bool = True, check_order: bool = True, rtol: float = 1e-05, atol: float = 1e-08, obj: str = 'Index') → None[source]¶

Check that left and right Index are equal.

Parameters:

left (Index)
right (Index)
exact (True) – Whether to check the Index class, dtype and inferred_type are identical.
check_names (bool, default True) – Whether to check the names attribute.
check_exact (bool, default True) – Whether to compare number exactly.
check_categorical (bool, default True) – Whether to compare internal Categorical exactly.
check_order (bool, default True) – Whether to compare the order of index entries as well as their values. If True, both indexes must contain the same elements, in the same order. If False, both indexes must contain the same elements, but in any order.
rtol (float, default 1e-5) – Relative tolerance. Only used when check_exact is False.
atol (float, default 1e-8) – Absolute tolerance. Only used when check_exact is False.
obj (str, default 'Index') – Specify object name being compared, internally used to show appropriate assertion message.

Examples

>>> from arkouda import testing as tm
>>> a = ak.Index([1, 2, 3])
>>> b = ak.Index([1, 2, 3])
>>> tm.assert_index_equal(a, b)

arkouda.assert_index_equivalent(left: arkouda.Index | pandas.Index, right: arkouda.Index | pandas.Index, exact: bool = True, check_names: bool = True, check_exact: bool = True, check_categorical: bool = True, check_order: bool = True, rtol: float = 1e-05, atol: float = 1e-08, obj: str = 'Index') → None[source]¶

Check that left and right Index are equal.

If the objects are pandas.Index, they are converted to arkouda.Index. Then assert_almost_equal is applied to the result.

Parameters:

left (Index or pandas.Index)
right (Index or pandas.Index)
exact (True) – Whether to check the Index class, dtype and inferred_type are identical.
check_names (bool, default True) – Whether to check the names attribute.
check_exact (bool, default True) – Whether to compare number exactly.
check_categorical (bool, default True) – Whether to compare internal Categorical exactly.
check_order (bool, default True) – Whether to compare the order of index entries as well as their values. If True, both indexes must contain the same elements, in the same order. If False, both indexes must contain the same elements, but in any order.
rtol (float, default 1e-5) – Relative tolerance. Only used when check_exact is False.
atol (float, default 1e-8) – Absolute tolerance. Only used when check_exact is False.
obj (str, default 'Index') – Specify object name being compared, internally used to show appropriate assertion message.

See also

assert_index_equal

Examples

>>> from arkouda import testing as tm
>>> import pandas as pd
>>> a = ak.Index([1, 2, 3])
>>> b = pd.Index([1, 2, 3])
>>> tm.assert_index_equivalent(a, b)

arkouda.assert_is_sorted(seq) → None[source]¶: Assert that the sequence is sorted.

arkouda.assert_series_equal(left, right, check_dtype: bool = True, check_index_type: bool = True, check_series_type: bool = True, check_names: bool = True, check_exact: bool = False, check_categorical: bool = True, check_category_order: bool = True, rtol: float = 1e-05, atol: float = 1e-08, obj: str = 'Series', *, check_index: bool = True, check_like: bool = False) → None[source]¶

Check that left and right Series are equal.

Parameters:

left (Series)
right (Series)
check_dtype (bool, default True) – Whether to check the Series dtype is identical.
check_index_type (bool, default True) – Whether to check the Index class, dtype and inferred_type are identical.
check_series_type (bool, default True) – Whether to check the Series class is identical.
check_names (bool, default True) – Whether to check the Series and Index names attribute.
check_exact (bool, default False) – Whether to compare number exactly.
check_categorical (bool, default True) – Whether to compare internal Categorical exactly.
check_category_order (bool, default True) – Whether to compare category order of internal Categoricals.
rtol (float, default 1e-5) – Relative tolerance. Only used when check_exact is False.
atol (float, default 1e-8) – Absolute tolerance. Only used when check_exact is False.
obj (str, default 'Series') – Specify object name being compared, internally used to show appropriate assertion message.
check_index (bool, default True) – Whether to check index equivalence. If False, then compare only values.
check_like (bool, default False) – If True, ignore the order of the index. Must be False if check_index is False. Note: same labels must be with the same data.

Examples

>>> from arkouda import testing as tm
>>> a = ak.Series([1, 2, 3, 4])
>>> b = ak.Series([1, 2, 3, 4])
>>> tm.assert_series_equal(a, b)

arkouda.assert_series_equivalent(left: arkouda.Series | pandas.Series, right: arkouda.Series | pandas.Series, check_dtype: bool = True, check_index_type: bool = True, check_series_type: bool = True, check_names: bool = True, check_exact: bool = False, check_categorical: bool = True, check_category_order: bool = True, rtol: float = 1e-05, atol: float = 1e-08, obj: str = 'Series', *, check_index: bool = True, check_like: bool = False) → None[source]¶

Check that left and right Series are equal.

pd.Series’s will be converted to the arkouda equivalent. Then assert_series_equal will be applied to the result.

Parameters:

left (Series or pd.Series)
right (Series or pd.Series)
check_dtype (bool, default True) – Whether to check the Series dtype is identical.
check_index_type (bool, default True) – Whether to check the Index class, dtype and inferred_type are identical.
check_series_type (bool, default True) – Whether to check the Series class is identical.
check_names (bool, default True) – Whether to check the Series and Index names attribute.
check_exact (bool, default False) – Whether to compare number exactly.
check_categorical (bool, default True) – Whether to compare internal Categorical exactly.
check_category_order (bool, default True) – Whether to compare category order of internal Categoricals.
rtol (float, default 1e-5) – Relative tolerance. Only used when check_exact is False.
atol (float, default 1e-8) – Absolute tolerance. Only used when check_exact is False.
obj (str, default 'Series') – Specify object name being compared, internally used to show appropriate assertion message.
check_index (bool, default True) – Whether to check index equivalence. If False, then compare only values.
check_like (bool, default False) – If True, ignore the order of the index. Must be False if check_index is False. Note: same labels must be with the same data.

See also

assert_series_equal

Examples

>>> from arkouda import testing as tm
>>> import pandas as pd
>>> a = ak.Series([1, 2, 3, 4])
>>> b = pd.Series([1, 2, 3, 4])
>>> tm.assert_series_equivalent(a, b)

arkouda.attach(name: str)[source]¶

arkouda.attach_all(names: list)[source]¶

Attach to all objects registered with the names provide

Parameters:: names (list) – List of names to attach to
Return type:: dict

arkouda.attach_pdarray(user_defined_name: str) → pdarray[source]¶

class method to return a pdarray attached to the registered name in the arkouda server which was registered using register()

Parameters:: user_defined_name (str) – user defined name which array was registered under
Returns:: pdarray which is bound to the corresponding server side component which was registered with user_defined_name
Return type:: pdarray
Raises:: TypeError – Raised if user_defined_name is not a str

Notes

Registered names/pdarrays in the server are immune to deletion until they are unregistered.

Examples

>>> a = zeros(100)
>>> a.register("my_zeros")
>>> # potentially disconnect from server and reconnect to server
>>> b = ak.attach_pdarray("my_zeros")
>>> # ...other work...
>>> b.unregister()

class arkouda.bigint[source]¶

Datatype for representing integers of variable size.

May be used for integers that exceed 64 bits.

itemsize = 128¶

kind = 'ui'¶

name = 'bigint'¶

ndim = 0¶

shape = ()¶

type(x)[source]¶

arkouda.bigint_from_uint_arrays(arrays, max_bits=-1)[source]¶

Create a bigint pdarray from an iterable of uint pdarrays. The first item in arrays will be the highest 64 bits and the last item will be the lowest 64 bits.

Parameters:

arrays (Sequence[pdarray]) – An iterable of uint pdarrays used to construct the bigint pdarray. The first item in arrays will be the highest 64 bits and the last item will be the lowest 64 bits.
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays

Returns:

bigint pdarray constructed from uint arrays

Return type:

pdarray.bigint_to_uint_arrays

Raises:

TypeError – Raised if any pdarray in arrays has a dtype other than uint or if the pdarrays are not the same size.
RuntimeError – Raised if there is a server-side error thrown

See also

Examples

>>> a = ak.bigint_from_uint_arrays([ak.ones(5, dtype=ak.uint64), ak.arange(5, dtype=ak.uint64)])
>>> a
array([18446744073709551616 18446744073709551617 18446744073709551618
18446744073709551619 18446744073709551620])

>>> a.dtype
dtype(bigint)

>>> all(a[i] == 2**64 + i for i in range(5))
True

arkouda.bitType¶

arkouda.bitType¶

arkouda.bool_scalars¶

arkouda.bool_scalars¶

arkouda.broadcast(segments: pdarray, values: pdarray | Strings, size: int | np.int64 | np.uint64 = -1, permutation: pdarray | None = None)[source]¶

Broadcast a dense column vector to the rows of a sparse matrix or grouped array.

Parameters:

segments (pdarray, int64) – Offsets of the start of each row in the sparse matrix or grouped array. Must be sorted in ascending order.
values (pdarray, Strings) – The values to broadcast, one per row (or group)
size (int) – The total number of nonzeros in the matrix. If permutation is given, this argument is ignored and the size is inferred from the permutation array.
permutation (pdarray, int64) – The permutation to go from the original ordering of nonzeros to the ordering grouped by row. To broadcast values back to the original ordering, this permutation will be inverted. If no permutation is supplied, it is assumed that the original nonzeros were already grouped by row. In this case, the size argument must be given.

Returns:

The broadcast values, one per nonzero

Return type:

Raises:

ValueError –

If segments and values are different sizes
If segments are empty
If number of nonzeros (either user-specified or inferred from permutation) is less than one

Examples

>>>
# Define a sparse matrix with 3 rows and 7 nonzeros
>>> row_starts = ak.array([0, 2, 5])
>>> nnz = 7
# Broadcast the row number to each nonzero element
>>> row_number = ak.arange(3)
>>> ak.broadcast(row_starts, row_number, nnz)
array([0 0 1 1 1 2 2])
# If the original nonzeros were in reverse order...
>>> permutation = ak.arange(6, -1, -1)
>>> ak.broadcast(row_starts, row_number, permutation=permutation)
array([2 2 1 1 1 0 0])

arkouda.broadcast(segments: pdarray, values: pdarray | Strings, size: int | np.int64 | np.uint64 = -1, permutation: pdarray | None = None)[source]¶

Broadcast a dense column vector to the rows of a sparse matrix or grouped array.

Parameters:

segments (pdarray, int64) – Offsets of the start of each row in the sparse matrix or grouped array. Must be sorted in ascending order.
values (pdarray, Strings) – The values to broadcast, one per row (or group)
size (int) – The total number of nonzeros in the matrix. If permutation is given, this argument is ignored and the size is inferred from the permutation array.
permutation (pdarray, int64) – The permutation to go from the original ordering of nonzeros to the ordering grouped by row. To broadcast values back to the original ordering, this permutation will be inverted. If no permutation is supplied, it is assumed that the original nonzeros were already grouped by row. In this case, the size argument must be given.

Returns:

The broadcast values, one per nonzero

Return type:

Raises:

ValueError –

If segments and values are different sizes
If segments are empty
If number of nonzeros (either user-specified or inferred from permutation) is less than one

Examples

>>>
# Define a sparse matrix with 3 rows and 7 nonzeros
>>> row_starts = ak.array([0, 2, 5])
>>> nnz = 7
# Broadcast the row number to each nonzero element
>>> row_number = ak.arange(3)
>>> ak.broadcast(row_starts, row_number, nnz)
array([0 0 1 1 1 2 2])
# If the original nonzeros were in reverse order...
>>> permutation = ak.arange(6, -1, -1)
>>> ak.broadcast(row_starts, row_number, permutation=permutation)
array([2 2 1 1 1 0 0])

arkouda.broadcast(segments: pdarray, values: pdarray | Strings, size: int | np.int64 | np.uint64 = -1, permutation: pdarray | None = None)[source]¶

Broadcast a dense column vector to the rows of a sparse matrix or grouped array.

Parameters:

segments (pdarray, int64) – Offsets of the start of each row in the sparse matrix or grouped array. Must be sorted in ascending order.
values (pdarray, Strings) – The values to broadcast, one per row (or group)
size (int) – The total number of nonzeros in the matrix. If permutation is given, this argument is ignored and the size is inferred from the permutation array.
permutation (pdarray, int64) – The permutation to go from the original ordering of nonzeros to the ordering grouped by row. To broadcast values back to the original ordering, this permutation will be inverted. If no permutation is supplied, it is assumed that the original nonzeros were already grouped by row. In this case, the size argument must be given.

Returns:

The broadcast values, one per nonzero

Return type:

see: https://data-apis.org/array-api/latest/API_specification/broadcasting.html#algorithm

Raises:

ValueError –

If segments and values are different sizes
If segments are empty
If number of nonzeros (either user-specified or inferred from permutation) is less than one

Examples

>>>
# Define a sparse matrix with 3 rows and 7 nonzeros
>>> row_starts = ak.array([0, 2, 5])
>>> nnz = 7
# Broadcast the row number to each nonzero element
>>> row_number = ak.arange(3)
>>> ak.broadcast(row_starts, row_number, nnz)
array([0 0 1 1 1 2 2])
# If the original nonzeros were in reverse order...
>>> permutation = ak.arange(6, -1, -1)
>>> ak.broadcast(row_starts, row_number, permutation=permutation)
array([2 2 1 1 1 0 0])

arkouda.broadcast_dims(sa: Sequence[int], sb: Sequence[int]) → Tuple[int, Ellipsis][source]¶

Algorithm to determine shape of broadcasted PD array given two array shapes

arkouda.broadcast_to_shape(pda: pdarray, shape: Tuple[int, Ellipsis]) → pdarray[source]¶

Create a “broadcasted” array (of rank ‘nd’) by copying an array into an array of the given shape.

E.g., given the following broadcast:

pda (3d array): 1 x 4 x 1

shape ( shape ): 7 x 4 x 2

Result (3d array): 7 x 4 x 2

When copying from a singleton dimension, the value is repeated along that dimension (e.g., pda’s 1st and 3rd above). For non singleton dimensions, the size of the two arrays must match, and the values are copied into the result array.

When prepending a new dimension to increase an array’s rank, the values from the other dimensions are repeated along the new dimension.

Parameters:

pda (pdarray) – the input to be broadcast
shape (tuple of int) – the shape to which pda is to be broadcast

Returns:

the result of the broadcast operation

Return type:

Examples

>>> a = ak.arange(2).reshape(1,2,1)
>>> ak.broadcast_to_shape(a,(2,2,2))
array([array([array([0 0]) array([1 1])]) array([array([0 0]) array([1 1])])])
>>> a = ak.array([5,19]).reshape(1,2)
>>> ak.broadcast_to_shape(a,(2,2,2))
array([array([array([5 19]) array([5 19])]) array([array([5 19]) array([5 19])])])

Raises:: RuntimeError – raised if the pda can’t be broadcast to the given shape

arkouda.can_cast(from_, to) → bool[source]¶

Returns True if cast between data types can occur according to the casting rule.

Parameters:

from (dtype, dtype specifier, NumPy scalar, or pdarray) – Data type, NumPy scalar, or array to cast from.
to (dtype or dtype specifier) – Data type to cast to.

Returns:

True if cast can occur according to the casting rule.

Return type:

bool

arkouda.cast(typ, val)[source]¶: Cast a value to a type.

This returns the value unchanged. To the type checker this signals that the return value has the designated type, but at runtime we intentionally don’t check anything (we want this to be as fast as possible).

arkouda.clear() → None[source]¶

Send a clear message to clear all unregistered data from the server symbol table

Return type:: None
Raises:: RuntimeError – Raised if there is a server-side error in executing clear request

arkouda.clz(pda: pdarray) → pdarray[source]¶

Count leading zeros for each integer in an array.

Parameters:: pda (pdarray, int64, uint64, bigint) – Input array (must be integral).
Returns:: lz – The number of leading zeros of each element.
Return type:: pdarray
Raises:: TypeError – If input array is not int64, uint64, or bigint

Examples

>>> A = ak.arange(10)
>>> ak.clz(A)
array([64, 63, 62, 62, 61, 61, 61, 61, 60, 60])

arkouda.coargsort(arrays: Sequence[arkouda.numpy.strings.Strings | arkouda.numpy.pdarrayclass.pdarray | arkouda.categorical.Categorical], algorithm: SortingAlgorithm = SortingAlgorithm.RadixSortLSD) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return the permutation that groups the rows (left-to-right), if the input arrays are treated as columns. The permutation sorts numeric columns, but not strings/Categoricals – strings/Categoricals are grouped, but not ordered.

Parameters:

arrays (Sequence of Strings, pdarray, or Categorical) – The columns (int64, uint64, float64, Strings, or Categorical) to sort by row
algorithm (SortingAlgorithm, default=SortingAlgorithm.RadixSortLSD) – The algorithm to be used for sorting the arrays.

Returns:

The indices that permute the rows to grouped order

Return type:

pdarray of int64

Raises:

ValueError – Raised if the pdarrays are not of the same size or if the parameter is not an Iterable containing pdarrays, Strings, or Categoricals

See also

argsort

Notes

Uses a least-significant-digit radix sort, which is stable and resilient to non-uniformity in data but communication intensive. Starts with the last array and moves forward. This sort operates directly on numeric types, but for Strings, it operates on a hash. Thus, while grouping of equivalent strings is guaranteed, lexicographic ordering of the groups is not. For Categoricals, coargsort sorts based on Categorical.codes which guarantees grouping of equivalent categories but not lexicographic ordering of those groups.

Examples

>>> a = ak.array([0, 1, 0, 1])
>>> b = ak.array([1, 1, 0, 0])
>>> perm = ak.coargsort([a, b])
>>> perm
array([2 0 3 1])
>>> a[perm]
array([0 0 1 1])
>>> b[perm]
array([0 1 0 1])

Parameters:

arrays (Sequence of Strings, pdarray, or Categorical) – The columns (int64, uint64, float64, Strings, or Categorical) to sort by row
algorithm (SortingAlgorithm, default=SortingAlgorithm.RadixSortLSD) – The algorithm to be used for sorting the arrays.

Returns:

The indices that permute the rows to grouped order

Return type:

pdarray of int64

Raises:

ValueError – Raised if the pdarrays are not of the same size or if the parameter is not an Iterable containing pdarrays, Strings, or Categoricals

See also

argsort

Notes

Examples

>>> a = ak.array([0, 1, 0, 1])
>>> b = ak.array([1, 1, 0, 0])
>>> perm = ak.coargsort([a, b])
>>> perm
array([2 0 3 1])
>>> a[perm]
array([0 0 1 1])
>>> b[perm]
array([0 1 0 1])

arkouda.concatenate(arrays: Sequence[arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.categorical.Categorical], ordered: bool = True) → arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.categorical.Categorical | Sequence[arkouda.categorical.Categorical][source]¶

Concatenate a list or tuple of pdarray or Strings objects into one pdarray or Strings object, respectively.

Parameters:

arrays (Sequence[Union[pdarray,Strings,Categorical]]) – The arrays to concatenate. Must all have same dtype.
ordered (bool) – If True (default), the arrays will be appended in the order given. If False, array data may be interleaved in blocks, which can greatly improve performance but results in non-deterministic ordering of elements.

Returns:

Single pdarray or Strings object containing all values, returned in the original order

Return type:

Union[pdarray,Strings,Categorical]

Raises:

ValueError – Raised if arrays is empty or if pdarrays have differing dtypes
TypeError – Raised if arrays is not a pdarrays or Strings python Sequence such as a list or tuple
RuntimeError – Raised if any array elements are dtypes for which concatenate has not been implemented.

Examples

>>> ak.concatenate([ak.array([1, 2, 3]), ak.array([4, 5, 6])])
array([1 2 3 4 5 6])

>>> ak.concatenate([ak.array([True,False,True]),ak.array([False,True,True])])
array([True False True False True True])

>>> ak.concatenate([ak.array(['one','two']),ak.array(['three','four','five'])])
array(['one', 'two', 'three', 'four', 'five'])

arkouda.convert_if_categorical(values)[source]¶: Convert a Categorical array to Strings for display

arkouda.corr(x: pdarray, y: pdarray) → numpy.float64[source]¶

Return the correlation between x and y

Parameters:

x (pdarray) – One of the pdarrays used to calculate correlation
y (pdarray) – One of the pdarrays used to calculate correlation

Returns:

The scalar correlation of the two pdarrays

Return type:

np.float64

Examples

>>> a = ak.arange(10)
>>> b = a + 1
>>> ak.corr(a,b)
0.9999999999999998
>>> a.corr(b)
0.9999999999999998

Raises:

TypeError – Raised if x or y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

See also

std, cov

Notes

The correlation is calculated by cov(x, y) / (x.std(ddof=1) * y.std(ddof=1))

arkouda.cov(x: pdarray, y: pdarray) → numpy.float64[source]¶

Return the covariance of x and y

Parameters:

x (pdarray) – One of the pdarrays used to calculate covariance
y (pdarray) – One of the pdarrays used to calculate covariance

Returns:

The scalar covariance of the two pdarrays

Return type:

np.float64

Examples

>>> a = ak.arange(10)
>>> b = a + 1
>>> ak.cov(a,b)
9.166666666666666
>>> a.cov(b)
9.166666666666666

Raises:

TypeError – Raised if x or y is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

See also

mean, var

Notes

The covariance is calculated by cov = ((x - x.mean()) * (y - y.mean())).sum() / (x.size - 1).

arkouda.create_pdarray(repMsg: str, max_bits=None) → pdarray[source]¶

Return a pdarray instance pointing to an array created by the arkouda server. The user should not call this function directly.

Parameters:

repMsg (str) – space-delimited string containing the pdarray name, datatype, size dimension, shape,and itemsize

Returns:

A pdarray with the same attributes and data as the pdarray; on GPU

Return type:

Raises:

ValueError – If there’s an error in parsing the repMsg parameter into the six values needed to create the pdarray instance
RuntimeError – Raised if a server-side error is thrown in the process of creating the pdarray instance

arkouda.create_pdarray(repMsg: str, max_bits=None) → pdarray[source]¶

Return a pdarray instance pointing to an array created by the arkouda server. The user should not call this function directly.

Parameters:

repMsg (str) – space-delimited string containing the pdarray name, datatype, size dimension, shape,and itemsize

Returns:

A pdarray with the same attributes and data as the pdarray; on GPU

Return type:

Raises:

ValueError – If there’s an error in parsing the repMsg parameter into the six values needed to create the pdarray instance
RuntimeError – Raised if a server-side error is thrown in the process of creating the pdarray instance

arkouda.create_pdarray(repMsg: str, max_bits=None) → pdarray[source]¶

Return a pdarray instance pointing to an array created by the arkouda server. The user should not call this function directly.

Parameters:

repMsg (str) – space-delimited string containing the pdarray name, datatype, size dimension, shape,and itemsize

Returns:

A pdarray with the same attributes and data as the pdarray; on GPU

Return type:

Raises:

ValueError – If there’s an error in parsing the repMsg parameter into the six values needed to create the pdarray instance
RuntimeError – Raised if a server-side error is thrown in the process of creating the pdarray instance

arkouda.ctz(pda: pdarray) → pdarray[source]¶

Count trailing zeros for each integer in an array.

Parameters:: pda (pdarray, int64, uint64, bigint) – Input array (must be integral).
Returns:: lz – The number of trailing zeros of each element.
Return type:: pdarray

Notes

ctz(0) is defined to be zero.

Raises:: TypeError – If input array is not int64, uint64, or bigint

Examples

>>> A = ak.arange(10)
>>> ak.ctz(A)
array([0, 0, 1, 0, 2, 0, 1, 0, 3, 0])

arkouda.date_operators(cls)[source]¶

arkouda.date_range(start=None, end=None, periods=None, freq=None, tz=None, normalize=False, name=None, closed=None, inclusive='both', **kwargs)[source]¶

Creates a fixed frequency Datetime range. Alias for ak.Datetime(pd.date_range(args)). Subject to size limit imposed by client.maxTransferBytes.

Parameters:

start (str or datetime-like, optional) – Left bound for generating dates.
end (str or datetime-like, optional) – Right bound for generating dates.
periods (int, optional) – Number of periods to generate.
freq (str or DateOffset, default 'D') – Frequency strings can have multiples, e.g. ‘5H’. See timeseries.offset_aliases for a list of frequency aliases.
tz (str or tzinfo, optional) – Time zone name for returning localized DatetimeIndex, for example ‘Asia/Hong_Kong’. By default, the resulting DatetimeIndex is timezone-naive.
normalize (bool, default False) – Normalize start/end dates to midnight before generating date range.
name (str, default None) – Name of the resulting DatetimeIndex.
closed ({None, 'left', 'right'}, optional) – Make the interval closed with respect to the given frequency to the ‘left’, ‘right’, or both sides (None, the default). Deprecated
inclusive ({"both", "neither", "left", "right"}, default "both") – Include boundaries. Whether to set each bound as closed or open.
**kwargs – For compatibility. Has no effect on the result.

Returns:

rng

Return type:

DatetimeIndex

Notes

Of the four parameters start, end, periods, and freq, exactly three must be specified. If freq is omitted, the resulting DatetimeIndex will have periods linearly spaced elements between start and end (closed on both sides).

To learn more about the frequency strings, please see this link.

arkouda.date_range(start=None, end=None, periods=None, freq=None, tz=None, normalize=False, name=None, closed=None, inclusive='both', **kwargs)[source]¶

Creates a fixed frequency Datetime range. Alias for ak.Datetime(pd.date_range(args)). Subject to size limit imposed by client.maxTransferBytes.

Parameters:

start (str or datetime-like, optional) – Left bound for generating dates.
end (str or datetime-like, optional) – Right bound for generating dates.
periods (int, optional) – Number of periods to generate.
freq (str or DateOffset, default 'D') – Frequency strings can have multiples, e.g. ‘5H’. See timeseries.offset_aliases for a list of frequency aliases.
tz (str or tzinfo, optional) – Time zone name for returning localized DatetimeIndex, for example ‘Asia/Hong_Kong’. By default, the resulting DatetimeIndex is timezone-naive.
normalize (bool, default False) – Normalize start/end dates to midnight before generating date range.
name (str, default None) – Name of the resulting DatetimeIndex.
closed ({None, 'left', 'right'}, optional) – Make the interval closed with respect to the given frequency to the ‘left’, ‘right’, or both sides (None, the default). Deprecated
inclusive ({"both", "neither", "left", "right"}, default "both") – Include boundaries. Whether to set each bound as closed or open.
**kwargs – For compatibility. Has no effect on the result.

Returns:

rng

Return type:

DatetimeIndex

Notes

To learn more about the frequency strings, please see this link.

arkouda.delete(arr: arkouda.numpy.pdarrayclass.pdarray, obj: arkouda.numpy.pdarrayclass.pdarray | slice | int, axis: int | None = None) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return a copy of ‘arr’ with elements along the specified axis removed.

Parameters:

arr (pdarray) – The array to remove elements from
obj (Union[pdarray, slice, int]) – The indices to remove from ‘arr’. If obj is a pdarray, it must have an integer dtype.
axis (Optional[int], optional) – The axis along which to remove elements. If None, the array will be flattened before removing elements. Defaults to None.

Returns:

A copy of ‘arr’ with elements removed

Return type:

arkouda.disableVerbose(logLevel: LogLevel = LogLevel.INFO) → None[source]¶

Disables verbose logging (DEBUG log level) for all ArkoudaLoggers, setting the log level for each to the logLevel parameter

Parameters:: logLevel (LogLevel) – The new log level, defaultts to LogLevel.INFO
Raises:: TypeError – Raised if logLevel is not a LogLevel enum

arkouda.divmod(x: arkouda.numpy.dtypes.numeric_scalars | pdarray, y: arkouda.numpy.dtypes.numeric_scalars | pdarray, where: arkouda.numpy.dtypes.bool_scalars | pdarray = True) → Tuple[pdarray, pdarray][source]¶

Parameters:

x (numeric_scalars(float_scalars, int_scalars) or pdarray) – The dividend array, the values that will be the numerator of the floordivision and will be acted on by the bases for modular division.
y (numeric_scalars(float_scalars, int_scalars) or pdarray) – The divisor array, the values that will be the denominator of the division and will be the bases for the modular division.
where (Boolean or pdarray) – This condition is broadcast over the input. At locations where the condition is True, the corresponding value will be divided using floor and modular division. Elsewhere, it will retain its original value. Default set to True.

Returns:

Returns a tuple that contains quotient and remainder of the division

Return type:

(pdarray, pdarray)

Raises:

TypeError – At least one entry must be a pdarray
ValueError – If both inputs are both pdarrays, their size must match
ZeroDivisionError – No entry in y is allowed to be 0, to prevent division by zero

Notes

The div is calculated by x // y The mod is calculated by x % y

Examples

>>> x = ak.arange(5, 10)
>>> y = ak.array([2, 1, 4, 5, 8])
>>> ak.divmod(x,y)
(array([2 6 1 1 1]), array([1 0 3 3 1]))
>>> ak.divmod(x,y, x % 2 == 0)
(array([5 6 7 1 9]), array([5 0 7 3 9]))

Returns the sum of the elementwise product of two arrays of the same size (the dot product) or the product of a singleton element and an array.

Parameters:

pda1 (Union[numeric_scalars, pdarray])
pda2 (Union[numeric_scalars, pdarray])

Returns:

The sum of the elementwise product pda1 and pda2 or the product of a singleton element and an array.

Return type:

Union[numeric_scalars, pdarray]

Raises:

ValueError – Raised if the size of pda1 is not the same as pda2

Examples

>>> x = ak.array([2, 3])
>>> y = ak.array([4, 5])
>>> ak.dot(x,y)
23
>>> ak.dot(x,2)
array([4 6])

arkouda.dtype(dtype)[source]¶

Create a data type object.

Parameters:: dtype (object) – Object to be converted to a data type object.
Return type:: type

arkouda.enableVerbose() → None[source]¶: Enables verbose logging (DEBUG log level) for all ArkoudaLoggers

arkouda.export(read_path: str, dataset_name: str = 'ak_data', write_file: str | None = None, return_obj: bool = True, index: bool = False)[source]¶

Export data from Arkouda file (Parquet/HDF5) to Pandas object or file formatted to be readable by Pandas

Parameters:

read_path (str) – path to file where arkouda data is stored.
dataset_name (str) – name to store dataset under
index (bool) – Default False. When True, maintain the indexes loaded from the pandas file
write_file (str, optional) – path to file to write pandas formatted data to. Only write the file if this is set
return_obj (bool, optional) – Default True. When True return the Pandas DataFrame object, otherwise return None

Raises:

RuntimeError –

Unsupported file type

Returns:

When return_obj=True

Return type:

pd.DataFrame

See also

pandas.DataFrame.to_parquet, pandas.DataFrame.to_hdf, pandas.DataFrame.read_parquet, pandas.DataFrame.read_hdf, ak.import_data

Notes

If Arkouda file is exported for pandas, the format will not change. This mean parquet files will remain parquet and hdf5 will remain hdf5.
Export can only be performed from hdf5 or parquet files written by Arkouda. The result will be the same file type, but formatted to be read by Pandas.

Reverse an array’s values along a particular axis or axes.

Parameters:

x (pdarray, Strings, or Categorical) –
Reverse the order of elements in an array along the given axis.

The shape of the array is preserved, but the elements are reordered.
axis (int or Tuple[int, ...], optional) – The axis or axes along which to flip the array. If None, flip the array along all axes.

Returns:

An array with the entries of axis reversed.

Return type:

pdarray, Strings, or Categorical

Note

This differs from numpy as it actually reverses the data, rather than presenting a view.

arkouda.float_scalars¶

arkouda.fmod(dividend: pdarray | arkouda.numpy.dtypes.numeric_scalars, divisor: pdarray | arkouda.numpy.dtypes.numeric_scalars) → pdarray[source]¶

Returns the element-wise remainder of division.

It is equivalent to np.fmod, the remainder has the same sign as the dividend.

Parameters:

dividend (numeric scalars or pdarray) – The array being acted on by the bases for the modular division.
divisor (numeric scalars or pdarray) – The array that will be the bases for the modular division.

Returns:

an array that contains the element-wise remainder of division.

Return type:

Raises:

TypeError – Raised if neither dividend nor divisor is a pdarray (at least one must be) or if any scalar or pdarray element is not one of int, uint, float, bigint

arkouda.from_series(series: pandas.Series, dtype: type | str | None = None) → arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings[source]¶

Converts a Pandas Series to an Arkouda pdarray or Strings object. If dtype is None, the dtype is inferred from the Pandas Series. Otherwise, the dtype parameter is set if the dtype of the Pandas Series is to be overridden or is unknown (for example, in situations where the Series dtype is object).

Parameters:

series (Pandas Series) – The Pandas Series with a dtype of bool, float64, int64, or string
dtype (Optional[type]) – The valid dtype types are np.bool, np.float64, np.int64, and np.str

Return type:

Union[pdarray,Strings]

Raises:

TypeError – Raised if series is not a Pandas Series object
ValueError – Raised if the Series dtype is not bool, float64, int64, string, datetime, or timedelta

Examples

>>> np.random.seed(1701)
>>> ak.from_series(pd.Series(np.random.randint(0,10,5)))
array([4 3 3 5 0])

>>> ak.from_series(pd.Series(['1', '2', '3', '4', '5']),dtype=np.int64)
array([1 2 3 4 5])

>>> np.random.seed(1701)
>>> ak.from_series(pd.Series(np.random.uniform(low=0.0,high=1.0,size=3)))
array([0.089433234324597599 0.1153776854774361 0.51874393620990389])

>>> ak.from_series(pd.Series(['0.57600036956445599', '0.41619265571741659',
                   '0.6615356693784662']), dtype=np.float64)
array([0.57600036956445599 0.41619265571741659 0.6615356693784662])

>>> np.random.seed(1864)
>>> ak.from_series(pd.Series(np.random.choice([True, False],size=5)))
array([True True True False False])

>>> ak.from_series(pd.Series(['True', 'False', 'False', 'True', 'True']), dtype=bool)
array([True True True True True])

>>> ak.from_series(pd.Series(['a', 'b', 'c', 'd', 'e'], dtype="string"))
array(['a', 'b', 'c', 'd', 'e'])

>>> ak.from_series(pd.Series(pd.to_datetime(['1/1/2018', np.datetime64('2018-01-01')])))
array([1514764800000000000 1514764800000000000])

Notes

The supported datatypes are bool, float64, int64, string, and datetime64[ns]. The data type is either inferred from the the Series or is set via the dtype parameter.

Series of datetime or timedelta are converted to Arkouda arrays of dtype int64 (nanoseconds)

A Pandas Series containing strings has a dtype of object. Arkouda assumes the Series contains strings and sets the dtype to str

Create a pdarray filled with fill_value.

Parameters:

size (int_scalars or tuple of int_scalars) – Size or shape of the array
fill_value (int_scalars or str) – Value with which the array will be filled
dtype (all_scalars) – Resulting array type, default float64
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays

Returns:

array of the requested size and dtype filled with fill_value

Return type:

Raises:

TypeError – Raised if the supplied dtype is not supported
RuntimeError – Raised if the size parameter is neither an int nor a str that is parseable to an int.
ValueError – Raised if the rank of the given shape is not in get_array_ranks() or is empty Raised if max_bits is not NONE and ndim does not equal 1

See also

zeros, ones

Examples

>>> ak.full(5, 7, dtype=ak.int64)
array([7 7 7 7 7])

>>> ak.full(5, 9, dtype=ak.float64)
array([9.00000000000000000 9.00000000000000000 9.00000000000000000
       9.00000000000000000 9.00000000000000000])

>>> ak.full(5, 5, dtype=ak.bool_)
array([True True True True True])

arkouda.full_like(pda: arkouda.numpy.pdarrayclass.pdarray, fill_value: arkouda.numpy.dtypes.numeric_scalars) → arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings[source]¶

Create a pdarray filled with fill_value of the same size and dtype as an existing pdarray.

Parameters:

pda (pdarray) – Array to use for size and dtype
fill_value (int_scalars) – Value with which the array will be filled

Returns:

Equivalent to ak.full(pda.size, fill_value, pda.dtype)

Return type:

ones_like, zeros_like

Raises:

TypeError – Raised if the pda parameter is not a pdarray.

See also

Notes

Logic for generating the pdarray is delegated to the ak.full method. Accordingly, the supported dtypes match are defined by the ak.full method.

Examples

>>> ak.full_like(ak.full(5,7,dtype=ak.int64),6)
array([6 6 6 6 6])

>>> ak.full_like(ak.full(7,9,dtype=ak.float64),10)
array([10.00000000000000000 10.00000000000000000 10.00000000000000000
       10.00000000000000000 10.00000000000000000 10.00000000000000000 10.00000000000000000])

>>> ak.full_like(ak.full(5,True,dtype=ak.bool_),False)
array([False False False False False])

arkouda.generic_concat(items, ordered=True)[source]¶

arkouda.generic_msg(cmd: str, args: Dict | None = None, payload: memoryview | None = None, send_binary: bool = False, recv_binary: bool = False) → str | memoryview[source]¶

Sends a binary or string message composed of a command and corresponding arguments to the arkouda_server, returning the response sent by the server.

Parameters:

cmd (str) – The server-side command to be executed
args (str) – A space-delimited list of command arguments
payload (memoryview) – The payload when sending binary data
send_binary (bool) – Indicates if the message to be sent is a string or binary
recv_binary (bool) – Indicates if the return message will be a string or binary

Returns:

The string or binary return message

Return type:

Union[str, memoryview]

Raises:

KeyboardInterrupt – Raised if the user interrupts during command execution
RuntimeError – Raised if the client is not connected to the server or if there is a server-side error thrown

Notes

If the server response is a string, the string corresponds to a success confirmation, warn message, or error message. A memoryview response corresponds to an Arkouda array output as a numpy array.

arkouda.getArkoudaLogger(name: str, handlers: List[logging.Handler] | None = None, logFormat: str | None = ArkoudaLogger.DEFAULT_LOG_FORMAT, logLevel: LogLevel | None = None) → ArkoudaLogger[source]¶

A convenience method for instantiating an ArkoudaLogger that retrieves the logging level from the ARKOUDA_LOG_LEVEL env variable

Parameters:

name (str) – The name of the ArkoudaLogger
handlers (List[Handler]) – A list of logging.Handler objects, if None, a list consisting of one StreamHandler named ‘console-handler’ is generated and configured
logFormat (str) – The format for log messages, defaults to the following format: ‘[%(name)s] Line %(lineno)d %(levelname)s: %(message)s’

Return type:

ArkoudaLogger

Raises:

TypeError – Raised if either name or logFormat is not a str object or if handlers is not a list of str objects

Notes

Important note: if a list of 1..n logging.Handler objects is passed in, and dynamic changes to 1..n handlers is desired, set a name for each Handler object as follows: handler.name = <desired name>, which will enable retrieval and updates for the specified handler.

arkouda.get_byteorder(dt: numpy.dtype) → str[source]¶

Get a concrete byteorder (turns ‘=’ into ‘<’ or ‘>’) on the client.

Parameters:: dt (np.dtype) – The numpy dtype to determine the byteorder of.
Returns:: Returns “<” for little endian and “>” for big endian.
Return type:: str
Raises:: ValueError – Returned if sys.byteorder is not “little” or “big”

Examples

>>> ak.get_byteorder(ak.dtype(ak.int64))
'<'

arkouda.get_callback(x)[source]¶

arkouda.get_columns(filenames: str | List[str], col_delim: str = ',', allow_errors: bool = False) → List[str][source]¶: Get a list of column names from CSV file(s).

arkouda.get_datasets(filenames: str | List[str], allow_errors: bool = False, column_delim: str = ',', read_nested: bool = True) → List[str][source]¶

Get the names of the datasets in the provide files

Parameters:

filenames (str or List[str]) – Name of the file/s from which to return datasets
allow_errors (bool) – Default: False Whether or not to allow errors while accessing datasets
column_delim (str) – Column delimiter to be used if dataset is CSV. Otherwise, unused.
read_nested (bool) – Default True, when True, SegArray objects will be read from the file. When False, SegArray (or other nested Parquet columns) will be ignored. Only used for Parquet Files.

Return type:

List[str] of names of the datasets

Raises:

RuntimeError –

If no datasets are returned

Notes

This function currently supports HDF5 and Parquet formats.
Future updates to Parquet will deprecate this functionality on that format,

but similar support will be added for Parquet at that time. - If a list of files is provided, only the datasets in the first file will be returned

See also

ls

arkouda.get_filetype(filenames: str | List[str]) → str[source]¶

Get the type of a file accessible to the server. Supported file types and possible return strings are ‘HDF5’ and ‘Parquet’.

Parameters:: filenames (Union[str, List[str]]) – A file or list of files visible to the arkouda server
Returns:: Type of the file returned as a string, either ‘HDF5’, ‘Parquet’ or ‘CSV
Return type:: str
Raises:: ValueError – Raised if filename is empty or contains only whitespace

Notes

When list provided, it is assumed that all files are the same type
CSV Files without the Arkouda Header are not supported

See also

read_parquet, read_hdf

arkouda.get_null_indices(filenames: str | List[str], datasets: str | List[str] | None = None) → arkouda.numpy.pdarrayclass.pdarray | Mapping[str, arkouda.numpy.pdarrayclass.pdarray][source]¶

Get null indices of a string column in a Parquet file.

Parameters:

filenames (list or str) – Either a list of filenames or shell expression
datasets (list or str or None) – (List of) name(s) of dataset(s) to read. Each dataset must be a string column. There is no default value for this function, the datasets to be read must be specified.

Returns:

Dictionary of {datasetName: pdarray}

Return type:

returns a dictionary of Arkouda pdarrays

Raises:

RuntimeError – Raised if one or more of the specified files cannot be opened.
TypeError – Raised if we receive an unknown arkouda_type returned from the server

See also

get_datasets, ls

arkouda.get_server_byteorder() → str[source]¶

Get the server’s byteorder

Returns:: Returns “little” for little endian and “big” for big endian.
Return type:: str
Raises:: ValueError – Raised if Server byteorder is not ‘little’ or ‘big’

Examples

>>> ak.get_server_byteorder()
'little'

arkouda.hist_all(ak_df: arkouda.dataframe.DataFrame, cols: list = [])[source]¶

Create a grid plot histogramming all numeric columns in ak dataframe

Parameters:

ak_df (ak.DataFrame) – Full Arkouda DataFrame containing data to be visualized
cols (list) – (Optional) A specified list of columns to be plotted

Notes

This function displays the plot.

Examples

>>> import arkouda as ak
>>> from arkouda.plotting import hist_all
>>> ak_df = ak.DataFrame({"a": ak.array(np.random.randn(100)),
                          "b": ak.array(np.random.randn(100)),
                          "c": ak.array(np.random.randn(100)),
                          "d": ak.array(np.random.randn(100))
                          })
>>> hist_all(ak_df)

arkouda.import_data(read_path: str, write_file: str | None = None, return_obj: bool = True, index: bool = False)[source]¶

Import data from a file saved by Pandas (HDF5/Parquet) to Arkouda object and/or a file formatted to be read by Arkouda.

Parameters:

read_path (str) – path to file where pandas data is stored. This can be glob expression for parquet formats.
write_file (str, optional) – path to file to write arkouda formatted data to. Only write file if provided
return_obj (bool, optional) – Default True. When True return the Arkouda DataFrame object, otherwise return None
index (bool, optional) – Default False. When True, maintain the indexes loaded from the pandas file

Raises:

RuntimeWarning –
- Export attempted on Parquet file. Arkouda formatted Parquet files are readable by pandas.
RuntimeError –
- Unsupported file type

Returns:

When return_obj=True

Return type:

pd.DataFrame

See also

pandas.DataFrame.to_parquet, pandas.DataFrame.to_hdf, pandas.DataFrame.read_parquet, pandas.DataFrame.read_hdf, ak.export

Notes

Import can only be performed from hdf5 or parquet files written by pandas.

arkouda.in1d(A: arkouda.groupbyclass.groupable, B: arkouda.groupbyclass.groupable, assume_unique: bool = False, symmetric: bool = False, invert: bool = False) → arkouda.groupbyclass.groupable[source]¶

Test whether each element of a 1-D array is also present in a second array.

Returns a boolean array the same length as A that is True where an element of A is in B and False otherwise.

Supports multi-level, i.e. test if rows of a are in the set of rows of b. But note that multi-dimensional pdarrays are not supported.

Parameters:

A (list of pdarrays, pdarray, Strings, or Categorical) – Entries will be tested for membership in B
B (list of pdarrays, pdarray, Strings, or Categorical) – The set of elements in which to test membership
assume_unique (bool, optional, defaults to False) – If true, assume rows of a and b are each unique and sorted. By default, sort and unique them explicitly.
symmetric (bool, optional, defaults to False) – Return in1d(A, B), in1d(B, A) when A and B are single items.
invert (bool, optional, defaults to False) – If True, the values in the returned array are inverted (that is, False where an element of A is in B and True otherwise). Default is False. ak.in1d(a, b, invert=True) is equivalent to (but is faster than) ~ak.in1d(a, b).

Returns:

True for each row in a that is contained in b

Return type:

pdarray, bool

Raises:

TypeError – Raised if either A or B is not a pdarray, Strings, or Categorical object, or if both are pdarrays and either has rank > 1, or if invert is not a bool
RuntimeError – Raised if the dtype of either array is not supported

Examples

>>> ak.in1d(ak.array([-1, 0, 1]), ak.array([-2, 0, 2]))
array([False True False])

>>> ak.in1d(ak.array(['one','two']),ak.array(['two', 'three','four','five']))
array([False True])

Notes

ak.in1d is not supported for bool or float64 pdarrays

Test whether each element of a 1-D array is also present in a second array.

Returns a boolean array the same length as A that is True where an element of A is in B and False otherwise.

Supports multi-level, i.e. test if rows of a are in the set of rows of b. But note that multi-dimensional pdarrays are not supported.

Parameters:

A (list of pdarrays, pdarray, Strings, or Categorical) – Entries will be tested for membership in B
B (list of pdarrays, pdarray, Strings, or Categorical) – The set of elements in which to test membership
assume_unique (bool, optional, defaults to False) – If true, assume rows of a and b are each unique and sorted. By default, sort and unique them explicitly.
symmetric (bool, optional, defaults to False) – Return in1d(A, B), in1d(B, A) when A and B are single items.
invert (bool, optional, defaults to False) – If True, the values in the returned array are inverted (that is, False where an element of A is in B and True otherwise). Default is False. ak.in1d(a, b, invert=True) is equivalent to (but is faster than) ~ak.in1d(a, b).

Returns:

True for each row in a that is contained in b

Return type:

pdarray, bool

Raises:

TypeError – Raised if either A or B is not a pdarray, Strings, or Categorical object, or if both are pdarrays and either has rank > 1, or if invert is not a bool
RuntimeError – Raised if the dtype of either array is not supported

Examples

>>> ak.in1d(ak.array([-1, 0, 1]), ak.array([-2, 0, 2]))
array([False True False])

>>> ak.in1d(ak.array(['one','two']),ak.array(['two', 'three','four','five']))
array([False True])

Notes

ak.in1d is not supported for bool or float64 pdarrays

arkouda.indexof1d(query: arkouda.groupbyclass.groupable, space: arkouda.groupbyclass.groupable) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return indices of query items in a search list of items. Items not found will be excluded. When duplicate terms are present in search space return indices of all occurrences.

Parameters:

query ((sequence of) pdarray or Strings or Categorical) – The items to search for. If multiple arrays, each “row” is an item.
space ((sequence of) pdarray or Strings or Categorical) – The set of items in which to search. Must have same shape/dtype as query.

Returns:

indices – For each item in query that is found in space, its index in space.

Return type:

pdarray, int64

Notes

This is an alias of ak.find(query, space, all_occurrences=True, remove_missing=True).values

Examples

>>> select_from = ak.arange(10)
>>> query = select_from[ak.randint(0, select_from.size, 20, seed=10)]
>>> space = select_from[ak.randint(0, select_from.size, 20, seed=11)]

remove some values to ensure that query has entries which don’t appear in space

>>> space = space[arr2 != 9]
>>> space = space[arr2 != 3]

>>> ak.indexof1d(query, space)
array([0 4 1 3 10 2 6 12 13 5 7 8 9 14 5 7 11 15 5 7 0 4])

Raises:

TypeError – Raised if either query or space is not a pdarray, Strings, or Categorical object
RuntimeError – Raised if the dtype of either array is not supported

arkouda.information(names: List[str] | str = RegisteredSymbols) → str[source]¶

Returns JSON formatted string containing information about the objects in names

Parameters:: names (Union[List[str], str]) – names is either the name of an object or list of names of objects to retrieve info if names is ak.AllSymbols, retrieves info for all symbols in the symbol table if names is ak.RegisteredSymbols, retrieves info for all symbols in the registry
Returns:: JSON formatted string containing a list of information for each object in names
Return type:: str
Raises:: RuntimeError – Raised if a server-side error is thrown in the process of retrieving information about the objects in names

arkouda.intTypes¶

arkouda.intTypes¶

arkouda.int_scalars¶

arkouda.intersect(a, b, positions=True, unique=False)[source]¶

Find the intersection of two arkouda arrays.

This function can be especially useful when positions=True so that the caller gets the indices of values present in both arrays.

Parameters:

a (Strings or pdarray) – An array of strings.
b (Strings or pdarray) – An array of strings.
positions (bool, default=True) – Return tuple of boolean pdarrays that indicate positions in a and b of the intersection values.
unique (bool, default=False) – If the number of distinct values in a (and b) is equal to the size of a (and b), there is a more efficient method to compute the intersection.

Returns:

(arkouda.numpy.pdarrayclass.pdarray, arkouda.numpy.pdarrayclass.pdarray) or
arkouda.numpy.pdarrayclass.pdarray – The indices of a and b where any element occurs at least once in both arrays.

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> a = ak.arange(10)
>>> print(a)
[0 1 2 3 4 5 6 7 8 9]

>>> b = 2 * ak.arange(10)
>>> print(b)
[0 2 4 6 8 10 12 14 16 18]

>>> intersect(a,b, positions=True)
(array([True False True False True False True False True False]),
array([True True True True True False False False False False]))

>>> intersect(a,b, positions=False)
array([0 2 4 6 8])

arkouda.intersect1d(A: arkouda.groupbyclass.groupable, B: arkouda.groupbyclass.groupable, assume_unique: bool = False) → arkouda.numpy.pdarrayclass.pdarray | arkouda.groupbyclass.groupable[source]¶

Find the intersection of two arrays.

Return the sorted, unique values that are in both of the input arrays.

Parameters:

A (list of pdarrays, pdarray, Strings, or Categorical)
B (list of pdarrays, pdarray, Strings, or Categorical)
assume_unique (bool) – If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False.

Returns:

Sorted 1D array/List of sorted pdarrays of common and unique elements.

Return type:

pdarray/groupable

Raises:

TypeError – Raised if either A or B is not a groupable
RuntimeError – Raised if the dtype of either pdarray is not supported

Examples

1D Example

>>> ak.intersect1d(ak.array([1, 3, 4, 3]), ak.array([3, 1, 2, 1]))
array([1 3])

Multi-Array Example

>>> a = ak.arange(5)
>>> b = ak.array([1, 5, 3, 4, 2])
>>> c = ak.array([1, 4, 3, 2, 5])
>>> d = ak.array([1, 2, 3, 5, 4])
>>> multia = [a, a, a]
>>> multib = [b, c, d]
>>> ak.intersect1d(multia, multib)
[array([1 3]), array([1 3]), array([1 3])]

arkouda.intx(a, b)[source]¶

Find all the rows that are in both dataframes. Columns should be in identical order.

Note: does not work for columns of floating point values, but does work for Strings, pdarrays of int64 type, and Categorical should work.

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> a = ak.DataFrame({'a':ak.arange(5),'b': 2* ak.arange(5)})
>>> display(a)

	a	b
0	0	0
1	1	2
2	2	4
3	3	6
4	4	8

>>> b = ak.DataFrame({'a':ak.arange(5),'b':ak.array([0,3,4,7,8])})
>>> display(b)

	a	b
0	0	0
1	1	3
2	2	4
3	3	7
4	4	8

>>> intx(a,b)
>>> intersect_df = a[intx(a,b)]
>>> display(intersect_df)

	a	b
0	0	0
1	2	4
2	4	8

arkouda.invert_permutation(perm)[source]¶

Find the inverse of a permutation array.

Parameters:: perm (pdarray) – The permutation array.
Returns:: The inverse of the permutation array.
Return type:: arkouda.numpy.pdarrayclass.pdarray

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> from arkouda.index import Index
>>> i = Index(ak.array([1,2,0,5,4]))
>>> perm = i.argsort()
>>> print(perm)
[2 0 1 4 3]
>>> invert_permutation(perm)
array([1 2 0 4 3])

arkouda.ip_address(values)[source]¶

Convert values to an Arkouda array of IP addresses.

Parameters:: values (list-like, integer pdarray, or IPv4) – The integer IP addresses or IPv4 object.
Returns:: The same IP addresses as an Arkouda array
Return type:: IPv4

Notes

This helper is intended to help future proof changes made to accomodate IPv6 and to prevent errors if a user inadvertently casts a IPv4 instead of a int64 pdarray. It can also be used for importing Python lists of IP addresses into Arkouda.

arkouda.isSupportedBool(num)[source]¶

Whether a scalar is an arkouda supported boolean dtype.

Parameters:: scalar (object)
Returns:: True if scalar is an instance of an arkouda supported boolean dtype, else False.
Return type:: bool

Examples

>>> ak.isSupportedBool(ak.int64)
False
>>> ak.isSupportedBool(bool)
True

arkouda.isSupportedDType(scalar: object) → bool[source]¶

Whether a scalar is an arkouda supported dtype.

Parameters:: scalar (object)
Returns:: True if scalar is an instance of an arkouda supported dtype, else False.
Return type:: bool

Examples

>>> ak.isSupportedDType(ak.int64)
True
>>> ak.isSupportedDType(np.complex128(1+2j))
False

arkouda.isSupportedFloat(num)[source]¶

Whether a scalar is an arkouda supported float dtype.

Parameters:: scalar (object)
Returns:: True if scalar is an instance of an arkouda supported float dtype, else False.
Return type:: bool

Examples

>>> ak.isSupportedFloat(ak.int64)
False
>>> ak.isSupportedFloat(ak.float64)
True

arkouda.isSupportedInt(num)[source]¶

Whether a scalar is an arkouda supported integer dtype.

Parameters:: scalar (object)
Returns:: True if scalar is an instance of an arkouda supported integer dtype, else False.
Return type:: bool

Examples

>>> ak.isSupportedInt(ak.int64)
True
>>> ak.isSupportedInt(ak.float64)
False

arkouda.isSupportedInt(num)[source]¶

Whether a scalar is an arkouda supported integer dtype.

Parameters:: scalar (object)
Returns:: True if scalar is an instance of an arkouda supported integer dtype, else False.
Return type:: bool

Examples

>>> ak.isSupportedInt(ak.int64)
True
>>> ak.isSupportedInt(ak.float64)
False

arkouda.isSupportedNumber(num)[source]¶

Whether a scalar is an arkouda supported numeric dtype.

Parameters:: scalar (object)
Returns:: True if scalar is an instance of an arkouda supported numeric dtype, else False.
Return type:: bool

Examples

>>> ak.isSupportedNumber(ak.int64)
True
>>> ak.isSupportedNumber(ak.str_)
False

arkouda.is_ipv4(ip: arkouda.numpy.pdarrayclass.pdarray | IPv4, ip2: arkouda.numpy.pdarrayclass.pdarray | None = None) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Indicate which values are ipv4 when passed data containing IPv4 and IPv6 values.

Parameters:

ip (pdarray (int64) or ak.IPv4)
in. (IPv4 value. High Bits of IPv6 if IPv6 is passed)
ip2 (pdarray (int64), Optional)
well. (Low Bits of IPv6. This is added for support when dealing with data that contains IPv6 as)

Return type:

pdarray of bools indicating which indexes are IPv4.

See also

ak.is_ipv6

arkouda.is_ipv6(ip: arkouda.numpy.pdarrayclass.pdarray | IPv4, ip2: arkouda.numpy.pdarrayclass.pdarray | None = None) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Indicate which values are ipv6 when passed data containing IPv4 and IPv6 values.

Parameters:

ip (pdarray (int64) or ak.IPv4)
IPv6. (High Bits of)
ip2 (pdarray (int64), Optional)
IPv6 (Low Bits of)

Return type:

pdarray of bools indicating which indexes are IPv6.

See also

ak.is_ipv4

arkouda.is_registered(name: str, as_component: bool = False) → bool[source]¶

Determine if the name provided is associated with a registered Object

Parameters:

name (str) – The name to check for in the registry
as_component (bool, default=False) – When True, the name will be checked to determine if it is registered as a component of a registered object

Return type:

bool

arkouda.linspace(start: arkouda.numpy.dtypes.numeric_scalars, stop: arkouda.numpy.dtypes.numeric_scalars, length: arkouda.numpy.dtypes.int_scalars) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Create a pdarray of linearly-spaced floats in a closed interval.

Parameters:

start (numeric_scalars) – Start of interval (inclusive)
stop (numeric_scalars) – End of interval (inclusive)
length (int_scalars) – Number of points

Returns:

Array of evenly spaced float values along the interval

Return type:

pdarray, float64

Raises:

TypeError – Raised if start or stop is not a float or int or if length is not an int

See also

arange

Notes

If that start is greater than stop, the pdarray values are generated in descending order.

Examples

>>> ak.linspace(0, 1, 5)
array([0.00000000000000000 0.25 0.5 0.75 1.00000000000000000])

>>> ak.linspace(start=1, stop=0, length=5)
array([1.00000000000000000 0.75 0.5 0.25 0.00000000000000000])

>>> ak.linspace(start=-5, stop=0, length=5)
array([-5.00000000000000000 -3.75 -2.5 -1.25 0.00000000000000000])

arkouda.list_registry(detailed: bool = False)[source]¶

Return a list containing the names of all registered objects

Parameters:: detailed (bool) – Default = False Return details of registry objects. Currently includes object type for any objects
Returns:: Dict containing keys “Components” and “Objects”.
Return type:: dict
Raises:: RuntimeError – Raised if there’s a server-side error thrown

arkouda.list_symbol_table() → List[str][source]¶

Return a list containing the names of all objects in the symbol table

Parameters:: None
Returns:: List of all object names in the symbol table
Return type:: list
Raises:: RuntimeError – Raised if there’s a server-side error thrown

arkouda.load(path_prefix: str, file_format: str = 'INFER', dataset: str = 'array', calc_string_offsets: bool = False, column_delim: str = ',') → Mapping[str, arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.numpy.timeclass.Datetime | arkouda.numpy.timeclass.Timedelta | arkouda.index.Index][source]¶

Load a pdarray previously saved with pdarray.save().

Parameters:

path_prefix (str) – Filename prefix used to save the original pdarray
file_format (str) – ‘INFER’, ‘HDF5’ or ‘Parquet’. Defaults to ‘INFER’. Used to indicate the file type being loaded. If INFER, this will be detected during processing
dataset (str) – Dataset name where the pdarray was saved, defaults to ‘array’
calc_string_offsets (bool) – If True the server will ignore Segmented Strings ‘offsets’ array and derive it from the null-byte terminators. Defaults to False currently
column_delim (str) – Column delimiter to be used if dataset is CSV. Otherwise, unused.

Returns:

Dictionary of {datsetName: Union[pdarray, Strings, SegArray, Categorical]} with the previously saved pdarrays, Strings, SegArrays, or Categoricals

Return type:

Mapping[str, Union[pdarray, Strings, SegArray, Categorical]]

Raises:

TypeError – Raised if either path_prefix or dataset is not a str
ValueError – Raised if invalid file_format or if the dataset is not present in all hdf5 files or if the path_prefix does not correspond to files accessible to Arkouda
RuntimeError – Raised if the hdf5 files are present but there is an error in opening one or more of them

Notes

If you have a previously saved Parquet file that is raising a FileNotFound error, try loading it with a .parquet appended to the prefix_path. Parquet files were previously ALWAYS stored with a .parquet extension.

ak.load does not support loading a single file. For loading single HDF5 files without the _LOCALE#### suffix please use ak.read().

CSV files without the Arkouda Header are not supported.

Examples

>>> # Loading from file without extension
>>> obj = ak.load('path/prefix')
Loads the array from numLocales files with the name ``cwd/path/name_prefix_LOCALE####``.
The file type is inferred during processing.

>>> # Loading with an extension (HDF5)
>>> obj = ak.load('path/prefix.test')
Loads the object from numLocales files with the name ``cwd/path/name_prefix_LOCALE####.test`` where
#### is replaced by each locale numbers. Because filetype is inferred during processing,
the extension is not required to be a specific format.

arkouda.load_all(path_prefix: str, file_format: str = 'INFER', column_delim: str = ',', read_nested=True) → Mapping[str, arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray | arkouda.categorical.Categorical][source]¶

Load multiple pdarrays, Strings, SegArrays, or Categoricals previously saved with save_all().

Parameters:

path_prefix (str) – Filename prefix used to save the original pdarray
file_format (str) – ‘INFER’, ‘HDF5’, ‘Parquet’, or ‘CSV’. Defaults to ‘INFER’. Indicates the format being loaded. When ‘INFER’ the processing will detect the format Defaults to ‘INFER’
column_delim (str) – Column delimiter to be used if dataset is CSV. Otherwise, unused.
read_nested (bool) – Default True, when True, SegArray objects will be read from the file. When False, SegArray (or other nested Parquet columns) will be ignored. Parquet files only

Returns:

Dictionary of {datsetName: Union[pdarray, Strings, SegArray, Categorical]} with the previously saved pdarrays, Strings, SegArrays, or Categoricals

Return type:

Mapping[str, Union[pdarray, Strings, SegArray, Categorical]]

Raises:

TypeError: – Raised if path_prefix is not a str
ValueError – Raised if file_format/extension is encountered that is not hdf5 or parquet or if all datasets are not present in all hdf5/parquet files or if the path_prefix does not correspond to files accessible to Arkouda
RuntimeError – Raised if the hdf5 files are present but there is an error in opening one or more of them

See also

to_parquet, to_hdf, load, read

Notes

This function has been updated to determine the file extension based on the file format variable

This function will be deprecated when glob flags are added to read_* methods

CSV files without the Arkouda Header are not supported.

arkouda.load_checkpoint(name, path='.akdata')[source]¶

Load server’s state. The server metadata must match the current configuration (e.g. same number of locales must be used).

Parameters:

name (str) – Name of the checkpoint. <path>/<name> must be a directory.
path (str) – The directory to save the checkpoint.

Returns:

The checkpoint name, which will be the same as the name argument.

Return type:

str

Examples

>>> arr = ak.zeros(10, int)
>>> arr[2] = 2
>>> arr[2]
2
>>> cp_name = ak.save_checkpoint()
>>> arr[2] = 3
>>> arr[2]
3
>>> ak.load_checkpoint(cp_name)
>>> arr[2]
2

See also

save_checkpoint

arkouda.ls(filename: str, col_delim: str = ',', read_nested: bool = True) → List[str][source]¶

This function calls the h5ls utility on a HDF5 file visible to the arkouda server or calls a function that imitates the result of h5ls on a Parquet file.

Parameters:

filename (str) – The name of the file to pass to the server
col_delim (str) – The delimiter used to separate columns if the file is a csv
read_nested (bool) – Default True, when True, SegArray objects will be read from the file. When False, SegArray (or other nested Parquet columns) will be ignored. Only used for Parquet files.

Returns:

The string output of the datasets from the server

Return type:

str

Raises:

TypeError – Raised if filename is not a str
ValueError – Raised if filename is empty or contains only whitespace
RuntimeError – Raised if error occurs in executing ls on an HDF5 file
Notes –
- This will need to be updated because Parquet will not technically support this when we update.
  Similar functionality will be added for Parquet in the future
- For CSV files without headers, please use ls_csv

See also

ls_csv

arkouda.ls_csv(filename: str, col_delim: str = ',') → List[str][source]¶

Used for identifying the datasets within a file when a CSV does not have a header.

Parameters:

filename (str) – The name of the file to pass to the server
col_delim (str) – The delimiter used to separate columns if the file is a csv

Returns:

The string output of the datasets from the server

Return type:

str

See also

ls

arkouda.maxk(pda: pdarray, k: arkouda.numpy.dtypes.int_scalars) → pdarray[source]¶

Find the k maximum values of an array.

Returns the largest k values of an array, sorted

Parameters:

pda (pdarray) – Input array.
k (int_scalars) – The desired count of maximum values to be returned by the output.

Returns:

The maximum k values from pda, sorted

Return type:

pdarray, int

Raises:

TypeError – Raised if pda is not a pdarray or k is not an integer
ValueError – Raised if the pda is empty, or pda.ndim > 1, or k < 1

Notes

This call is equivalent in value to a[ak.argsort(a)[k:]]

and generally outperforms this operation.

Examples

>>> A = ak.array([10,5,1,3,7,2,9,0])
>>> ak.maxk(A, 3)
array([7, 9, 10])
>>> ak.maxk(A, 4)
array([5, 7, 9, 10])

arkouda.mean(pda: pdarray) → numpy.float64[source]¶

Return the mean of the array.

Parameters:: pda (pdarray) – Values for which to calculate the mean
Returns:: The mean calculated from the pda sum and size
Return type:: np.float64

Examples

>>> a = ak.arange(10)
>>> ak.mean(a)
4.5
>>> a.mean()
4.5

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

arkouda.merge(left: DataFrame, right: DataFrame, on: str | List[str] | None = None, how: str = 'inner', left_suffix: str = '_x', right_suffix: str = '_y', convert_ints: bool = True, sort: bool = True) → DataFrame[source]¶

Merge Arkouda DataFrames with a database-style join. The resulting dataframe contains rows from both DataFrames as specified by the merge condition (based on the “how” and “on” parameters).

Based on pandas merge functionality. https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html

Parameters:

left (DataFrame) – The Left DataFrame to be joined.
right (DataFrame) – The Right DataFrame to be joined.
on (Optional[Union[str, List[str]]] = None) – The name or list of names of the DataFrame column(s) to join on. If on is None, this defaults to the intersection of the columns in both DataFrames.
how (str, default = "inner") – The merge condition. Must be one of “inner”, “left”, “right”, or “outer”.
left_suffix (str, default = "_x") – A string indicating the suffix to add to columns from the left dataframe for overlapping column names in both left and right. Defaults to “_x”. Only used when how is “inner”.
right_suffix (str, default = "_y") – A string indicating the suffix to add to columns from the right dataframe for overlapping column names in both left and right. Defaults to “_y”. Only used when how is “inner”.
convert_ints (bool = True) – If True, convert columns with missing int values (due to the join) to float64. This is to match pandas. If False, do not convert the column dtypes. This has no effect when how = “inner”.
sort (bool = True) – If True, DataFrame is returned sorted by “on”. Otherwise, the DataFrame is not sorted.

Returns:

Joined Arkouda DataFrame.

Return type:

Note

Multiple column joins are only supported for integer columns.

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> from arkouda import merge
>>> left_df = ak.DataFrame({'col1': ak.arange(5), 'col2': -1 * ak.arange(5)})
>>> display(left_df)

	col1	col2
0	0	0
1	1	-1
2	2	-2
3	3	-3
4	4	-4

>>> right_df = ak.DataFrame({'col1': 2 * ak.arange(5), 'col2': 2 * ak.arange(5)})
>>> display(right_df)

	col1	col2
0	0	0
1	2	2
2	4	4
3	6	6
4	8	8

>>> merge(left_df, right_df, on = "col1")

	col1	col2_x	col2_y
0	0	0	0
1	2	-2	2
2	4	-4	4

>>> merge(left_df, right_df, on = "col1", how = "left")

	col1	col2_y	col2_x
0	0	0	0
1	1	nan	-1
2	2	2	-2
3	3	nan	-3
4	4	4	-4

>>> merge(left_df, right_df, on = "col1", how = "right")

	col1	col2_x	col2_y
0	0	0	0
1	2	-2	2
2	4	-4	4
3	6	nan	6
4	8	nan	8

>>> merge(left_df, right_df, on = "col1", how = "outer")

	col1	col2_y	col2_x
0	0	0	0
1	1	nan	-1
2	2	2	-2
3	3	nan	-3
4	4	4	-4
5	6	6	nan
6	8	8	nan

arkouda.mink(pda: pdarray, k: arkouda.numpy.dtypes.int_scalars) → pdarray[source]¶

Find the k minimum values of an array.

Returns the smallest k values of an array, sorted

Parameters:

pda (pdarray) – Input array.
k (int_scalars) – The desired count of minimum values to be returned by the output.

Returns:

The minimum k values from pda, sorted

Return type:

Raises:

TypeError – Raised if pda is not a pdarray
ValueError – Raised if the pda is empty, or pda.ndim > 1, or k < 1

Notes

This call is equivalent in value to a[ak.argsort(a)[:k]] and generally outperforms this operation.

Examples

>>> A = ak.array([10,5,1,3,7,2,9,0])
>>> ak.mink(A, 3)
array([0, 1, 2])
>>> ak.mink(A, 4)
array([0, 1, 2, 3])

arkouda.mod(dividend, divisor) → pdarray[source]¶

Returns the element-wise remainder of division.

Computes the remainder complementary to the floor_divide function. It is equivalent to np.mod, the remainder has the same sign as the divisor.

Parameters:

dividend – pdarray : The numeric scalar or pdarray being acted on by the bases for the modular division.
divisor – pdarray : The numeric scalar or pdarray that will be the bases for the modular division.

Returns:

an array that contains the element-wise remainder of division.

Return type:

Examples

>>> a = ak.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20])
>>> b = ak.array([2,2,2,3,3,3,4,4,4,5,5,5,6,6,6,7,7,7,8,8])
>>> ak.mod(a,b)
array([1 0 1 1 2 0 3 0 1 0 1 2 1 2 3 2 3 4 3 4])

Raises:: ValueError – raised if shapes of dividend and divisor are incompatible

class arkouda.ndarray¶

ndarray(shape, dtype=float, buffer=None, offset=0,

strides=None, order=None)

An array object represents a multidimensional, homogeneous array of fixed-size items. An associated data-type object describes the format of each element in the array (its byte-order, how many bytes it occupies in memory, whether it is an integer, a floating point number, or something else, etc.)

Arrays should be constructed using array, zeros or empty (refer to the See Also section below). The parameters given here refer to a low-level method (ndarray(…)) for instantiating an array.

For more information, refer to the numpy module and examine the methods and attributes of an array.

(for the __new__ method; see Notes below)

shapetuple of ints: Shape of created array.
dtypedata-type, optional: Any object that can be interpreted as a numpy data type.
bufferobject exposing buffer interface, optional: Used to fill the array with data.
offsetint, optional: Offset of array data in buffer.
stridestuple of ints, optional: Strides of data in memory.
order{‘C’, ‘F’}, optional: Row-major (C-style) or column-major (Fortran-style) order.

Tndarray: Transpose of the array.
databuffer: The array’s elements, in memory.
dtypedtype object: Describes the format of the elements in the array.
flagsdict: Dictionary containing information related to memory use, e.g., ‘C_CONTIGUOUS’, ‘OWNDATA’, ‘WRITEABLE’, etc.
flatnumpy.flatiter object: Flattened version of the array as an iterator. The iterator allows assignments, e.g., x.flat = 3 (See ndarray.flat for assignment examples; TODO).
imagndarray: Imaginary part of the array.
realndarray: Real part of the array.
sizeint: Number of elements in the array.
itemsizeint: The memory use of each array element in bytes.
nbytesint: The total number of bytes required to store the array data, i.e., itemsize * size.
ndimint: The array’s number of dimensions.
shapetuple of ints: Shape of the array.
stridestuple of ints: The step-size required to move from one element to the next in memory. For example, a contiguous (3, 4) array of type int16 in C-order has strides (8, 2). This implies that to move from element to element in memory requires jumps of 2 bytes. To move from row-to-row, one needs to jump 8 bytes at a time (2 * 4).
ctypesctypes object: Class containing properties of the array needed for interaction with ctypes.
basendarray: If the array is a view into another array, that array is its base (unless that array is also a view). The base array is where the array data is actually stored.

array : Construct an array. zeros : Create an array, each element of which is zero. empty : Create an array, but leave its allocated memory unchanged (i.e.,

it contains “garbage”).

dtype : Create a data-type. numpy.typing.NDArray : An ndarray alias generic

w.r.t. its dtype.type <numpy.dtype.type>.

There are two modes of creating an array using __new__:

If buffer is None, then only shape, dtype, and order are used.
If buffer is an object exposing the buffer interface, then all keywords are interpreted.

No __init__ method is needed because the array is fully initialized after the __new__ method.

These examples illustrate the low-level ndarray constructor. Refer to the See Also section above for easier ways of constructing an ndarray.

First mode, buffer is None:

>>> np.ndarray(shape=(2,2), dtype=float, order='F')
array([[0.0e+000, 0.0e+000], # random
       [     nan, 2.5e-323]])

Second mode:

>>> np.ndarray((2,), buffer=np.array([1,2,3]),
...            offset=np.int_().itemsize,
...            dtype=int) # offset = 1*itemsize, i.e. skip first element
array([2, 3])

T(*args, **kwargs)¶

View of the transposed array.

Same as self.transpose().

>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
       [3, 4]])
>>> a.T
array([[1, 3],
       [2, 4]])

>>> a = np.array([1, 2, 3, 4])
>>> a
array([1, 2, 3, 4])
>>> a.T
array([1, 2, 3, 4])

transpose

all(*args, **kwargs)¶: a.all(axis=None, out=None, keepdims=False, *, where=True)

Returns True if all elements evaluate to True.

Refer to numpy.all for full documentation.

numpy.all : equivalent function

any(*args, **kwargs)¶: a.any(axis=None, out=None, keepdims=False, *, where=True)

Returns True if any of the elements of a evaluate to True.

Refer to numpy.any for full documentation.

numpy.any : equivalent function

argmax(*args, **kwargs)¶: a.argmax(axis=None, out=None, *, keepdims=False)

Return indices of the maximum values along the given axis.

Refer to numpy.argmax for full documentation.

numpy.argmax : equivalent function

argmin(*args, **kwargs)¶: a.argmin(axis=None, out=None, *, keepdims=False)

Return indices of the minimum values along the given axis.

Refer to numpy.argmin for detailed documentation.

numpy.argmin : equivalent function

argpartition(*args, **kwargs)¶: a.argpartition(kth, axis=-1, kind=’introselect’, order=None)

Returns the indices that would partition this array.

Refer to numpy.argpartition for full documentation.

Added in version 1.8.0.

numpy.argpartition : equivalent function

argsort(*args, **kwargs)¶: a.argsort(axis=-1, kind=None, order=None)

Returns the indices that would sort this array.

Refer to numpy.argsort for full documentation.

numpy.argsort : equivalent function

astype(*args, **kwargs)¶

a.astype(dtype, order=’K’, casting=’unsafe’, subok=True, copy=True)

Copy of the array, cast to a specified type.

dtypestr or dtype
Typecode or data-type to which the array is cast.

order{‘C’, ‘F’, ‘A’, ‘K’}, optional
Controls the memory layout order of the result. ‘C’ means C order, ‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to the order the array elements appear in memory as possible. Default is ‘K’.

casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional
Controls what kind of data casting may occur. Defaults to ‘unsafe’ for backwards compatibility.

‘no’ means the data types should not be cast at all.

‘equiv’ means only byte-order changes are allowed.

‘safe’ means only casts which can preserve values are allowed.

‘same_kind’ means only safe casts or casts within a kind, like float64 to float32, are allowed.

‘unsafe’ means any data conversions may be done.

subokbool, optional
If True, then sub-classes will be passed-through (default), otherwise the returned array will be forced to be a base-class array.

copybool, optional
By default, astype always returns a newly allocated array. If this is set to false, and the dtype, order, and subok requirements are satisfied, the input array is returned instead of a copy.

arr_tndarray
Unless copy is False and the other conditions for returning the input array are satisfied (see description for copy input parameter), arr_t is a new array of the same shape as the input array, with dtype, order given by dtype, order.

Changed in version 1.17.0: Casting between a simple data type and a structured one is possible only for “unsafe” casting. Casting to multiple fields is allowed, but casting from multiple fields is not.

Changed in version 1.9.0: Casting from numeric to string types in ‘safe’ casting mode requires that the string dtype length is long enough to store the max integer/float value converted.

ComplexWarning
When casting from complex to float or int. To avoid this, one should use a.real.astype(t).
>>> x = np.array([1, 2, 2.5])
>>> x
array([1. ,  2. ,  2.5])
>>> x.astype(int)
array([1, 2, 2])

base(*args, **kwargs)¶

Base object if memory is from some other object.

The base of an array that owns its memory is None:
>>> x = np.array([1,2,3,4])
>>> x.base is None
True
Slicing creates a view, whose memory is shared with x:
>>> y = x[2:]
>>> y.base is x
True

byteswap(*args, **kwargs)¶

a.byteswap(inplace=False)

Swap the bytes of the array elements

Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place. Arrays of byte-strings are not swapped. The real and imaginary parts of a complex number are swapped individually.

inplacebool, optional
If True, swap bytes in-place, default is False.

outndarray
The byteswapped array. If inplace is True, this is a view to self.
>>> A = np.array([1, 256, 8755], dtype=np.int16)
>>> list(map(hex, A))
['0x1', '0x100', '0x2233']
>>> A.byteswap(inplace=True)
array([  256,     1, 13090], dtype=int16)
>>> list(map(hex, A))
['0x100', '0x1', '0x3322']
Arrays of byte-strings are not swapped
>>> A = np.array([b'ceg', b'fac'])
>>> A.byteswap()
array([b'ceg', b'fac'], dtype='|S3')
A.newbyteorder().byteswap() produces an array with the same values
but different representation in memory
>>> A = np.array([1, 2, 3])
>>> A.view(np.uint8)
array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0,
       0, 0], dtype=uint8)
>>> A.newbyteorder().byteswap(inplace=True)
array([1, 2, 3])
>>> A.view(np.uint8)
array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0,
       0, 3], dtype=uint8)

choose(*args, **kwargs)¶: a.choose(choices, out=None, mode=’raise’)

Use an index array to construct a new array from a set of choices.

Refer to numpy.choose for full documentation.

numpy.choose : equivalent function

clip(*args, **kwargs)¶: a.clip(min=None, max=None, out=None, **kwargs)

Return an array whose values are limited to [min, max]. One of max or min must be given.

Refer to numpy.clip for full documentation.

numpy.clip : equivalent function

compress(*args, **kwargs)¶: a.compress(condition, axis=None, out=None)

Return selected slices of this array along given axis.

Refer to numpy.compress for full documentation.

numpy.compress : equivalent function

conj(*args, **kwargs)¶: a.conj()

Complex-conjugate all elements.

Refer to numpy.conjugate for full documentation.

numpy.conjugate : equivalent function

conjugate(*args, **kwargs)¶: a.conjugate()

Return the complex conjugate, element-wise.

Refer to numpy.conjugate for full documentation.

numpy.conjugate : equivalent function

copy(*args, **kwargs)¶

a.copy(order=’C’)

Return a copy of the array.

order{‘C’, ‘F’, ‘A’, ‘K’}, optional
Controls the memory layout of the copy. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. (Note that this function and numpy.copy() are very similar but have different default values for their order= arguments, and this function always passes sub-classes through.)

numpy.copy : Similar function with different default behavior numpy.copyto

This function is the preferred method for creating an array copy. The function numpy.copy() is similar, but it defaults to using order ‘K’, and will not pass sub-classes through by default.
>>> x = np.array([[1,2,3],[4,5,6]], order='F')
>>> y = x.copy()
>>> x.fill(0)
>>> x
array([[0, 0, 0],
       [0, 0, 0]])
>>> y
array([[1, 2, 3],
       [4, 5, 6]])
>>> y.flags['C_CONTIGUOUS']
True

ctypes(*args, **kwargs)¶

An object to simplify the interaction of the array with the ctypes

module.

This attribute creates an object that makes it easier to use arrays when calling shared libraries with the ctypes module. The returned object has, among others, data, shape, and strides attributes (see Notes below) which themselves return ctypes objects that can be used as arguments to a shared library.

None

cPython object: Possessing attributes data, shape, strides, etc.

numpy.ctypeslib

Below are the public attributes of this object which were documented in “Guide to NumPy” (we have omitted undocumented public attributes, as well as documented private attributes):

_ctypes.data

A pointer to the memory area of the array as a Python integer. This memory area may contain data that is not aligned, or not in correct byte-order. The memory area may not even be writeable. The array flags and data-type of this array should be respected when passing this attribute to arbitrary C-code to avoid trouble that can include Python crashing. User Beware! The value of this attribute is exactly the same as self._array_interface_['data'][0].

Note that unlike data_as, a reference will not be kept to the array: code like ctypes.c_void_p((a + b).ctypes.data) will result in a pointer to a deallocated array, and should be spelt (a + b).ctypes.data_as(ctypes.c_void_p)

_ctypes.shape

A ctypes array of length self.ndim where the basetype is the C-integer corresponding to dtype('p') on this platform (see ~numpy.ctypeslib.c_intp). This base-type could be ctypes.c_int, ctypes.c_long, or ctypes.c_longlong depending on the platform. The ctypes array contains the shape of the underlying array.

Type:: (c_intp*self.ndim)

_ctypes.strides

A ctypes array of length self.ndim where the basetype is the same as for the shape attribute. This ctypes array contains the strides information from the underlying array. This strides information is important for showing how many bytes must be jumped to get to the next element in the array.

Type:: (c_intp*self.ndim)

_ctypes.data_as(obj)

Return the data pointer cast to a particular c-types object. For example, calling self._as_parameter_ is equivalent to self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a pointer to a ctypes array of floating-point data: self.data_as(ctypes.POINTER(ctypes.c_double)).

The returned pointer will keep a reference to the array.

_ctypes.shape_as(obj): Return the shape tuple as an array of some other c-types type. For example: self.shape_as(ctypes.c_short).

_ctypes.strides_as(obj): Return the strides tuple as an array of some other c-types type. For example: self.strides_as(ctypes.c_longlong).

If the ctypes module is not available, then the ctypes attribute of array objects still returns something useful, but ctypes objects are not returned and errors may be raised instead. In particular, the object will still have the as_parameter attribute which will return an integer equal to the data attribute.

>>> import ctypes
>>> x = np.array([[0, 1], [2, 3]], dtype=np.int32)
>>> x
array([[0, 1],
       [2, 3]], dtype=int32)
>>> x.ctypes.data
31962608 # may vary
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32))
<__main__.LP_c_uint object at 0x7ff2fc1fc200> # may vary
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)).contents
c_uint(0)
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint64)).contents
c_ulong(4294967296)
>>> x.ctypes.shape
<numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1fce60> # may vary
>>> x.ctypes.strides
<numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1ff320> # may vary

cumprod(*args, **kwargs)¶: a.cumprod(axis=None, dtype=None, out=None)

Return the cumulative product of the elements along the given axis.

Refer to numpy.cumprod for full documentation.

numpy.cumprod : equivalent function

cumsum(*args, **kwargs)¶: a.cumsum(axis=None, dtype=None, out=None)

Return the cumulative sum of the elements along the given axis.

Refer to numpy.cumsum for full documentation.

numpy.cumsum : equivalent function

data(*args, **kwargs)¶: Python buffer object pointing to the start of the array’s data.

diagonal(*args, **kwargs)¶: a.diagonal(offset=0, axis1=0, axis2=1)

Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed.

Refer to numpy.diagonal() for full documentation.

numpy.diagonal : equivalent function

dot(*args, **kwargs)¶

dtype(*args, **kwargs)¶

Data-type of the array’s elements.

Warning

Setting arr.dtype is discouraged and may be deprecated in the future. Setting will replace the dtype without modifying the memory (see also ndarray.view and ndarray.astype).

None

d : numpy dtype object

ndarray.astype : Cast the values contained in the array to a new data-type. ndarray.view : Create a view of the same data but a different data-type. numpy.dtype
>>> x
array([[0, 1],
 [2, 3]])
>>> x.dtype
dtype('int32')
>>> type(x.dtype)
<type 'numpy.dtype'>

dump(*args, **kwargs)¶

a.dump(file)

Dump a pickle of the array to the specified file. The array can be read back with pickle.load or numpy.load.

filestr or Path
A string naming the dump file.

Changed in version 1.17.0: pathlib.Path objects are now accepted.

dumps(*args, **kwargs)¶: a.dumps()

Returns the pickle of the array as a string. pickle.loads will convert the string back to an array.

None

fill(*args, **kwargs)¶

a.fill(value)

Fill the array with a scalar value.

valuescalar
All elements of a will be assigned this value.
>>> a = np.array([1, 2])
>>> a.fill(0)
>>> a
array([0, 0])
>>> a = np.empty(2)
>>> a.fill(1)
>>> a
array([1.,  1.])
Fill expects a scalar value and always behaves the same as assigning to a single array element. The following is a rare example where this distinction is important:
>>> a = np.array([None, None], dtype=object)
>>> a[0] = np.array(3)
>>> a
array([array(3), None], dtype=object)
>>> a.fill(np.array(3))
>>> a
array([array(3), array(3)], dtype=object)
Where other forms of assignments will unpack the array being assigned:
>>> a[...] = np.array(3)
>>> a
array([3, 3], dtype=object)

flags(*args, **kwargs)¶

Information about the memory layout of the array.

C_CONTIGUOUS (C)
The data is in a single, C-style contiguous segment.

F_CONTIGUOUS (F)
The data is in a single, Fortran-style contiguous segment.

OWNDATA (O)
The array owns the memory it uses or borrows it from another object.

WRITEABLE (W)
The data area can be written to. Setting this to False locks the data, making it read-only. A view (slice, etc.) inherits WRITEABLE from its base array at creation time, but a view of a writeable array may be subsequently locked while the base array remains writeable. (The opposite is not true, in that a view of a locked array may not be made writeable. However, currently, locking a base object does not lock any views that already reference it, so under that circumstance it is possible to alter the contents of a locked array via a previously created writeable view onto it.) Attempting to change a non-writeable array raises a RuntimeError exception.

ALIGNED (A)
The data and all elements are aligned appropriately for the hardware.

WRITEBACKIFCOPY (X)
This array is a copy of some other array. The C-API function PyArray_ResolveWritebackIfCopy must be called before deallocating to the base array will be updated with the contents of this array.

FNC
F_CONTIGUOUS and not C_CONTIGUOUS.

FORC
F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).

BEHAVED (B)
ALIGNED and WRITEABLE.

CARRAY (CA)
BEHAVED and C_CONTIGUOUS.

FARRAY (FA)
BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.

The flags object can be accessed dictionary-like (as in a.flags['WRITEABLE']), or by using lowercased attribute names (as in a.flags.writeable). Short flag names are only supported in dictionary access.

Only the WRITEBACKIFCOPY, WRITEABLE, and ALIGNED flags can be changed by the user, via direct assignment to the attribute or dictionary entry, or by calling ndarray.setflags.

The array flags cannot be set arbitrarily:

WRITEBACKIFCOPY can only be set False.

ALIGNED can only be set True if the data is truly aligned.

WRITEABLE can only be set True if the array owns its own memory or the ultimate owner of the memory exposes a writeable buffer interface or is a string.

Arrays can be both C-style and Fortran-style contiguous simultaneously. This is clear for 1-dimensional arrays, but can also be true for higher dimensional arrays.

Even for contiguous arrays a stride for a given dimension arr.strides[dim] may be arbitrary if arr.shape[dim] == 1 or the array has no elements. It does not generally hold that self.strides[-1] == self.itemsize for C-style contiguous arrays or self.strides[0] == self.itemsize for Fortran-style contiguous arrays is true.

flat(*args, **kwargs)¶

A 1-D iterator over the array.

This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python’s built-in iterator object.

flatten : Return a copy of the array collapsed into one dimension.

flatiter
>>> x = np.arange(1, 7).reshape(2, 3)
>>> x
array([[1, 2, 3],
 [4, 5, 6]])
>>> x.flat[3]
4
>>> x.T
array([[1, 4],
 [2, 5],
 [3, 6]])
>>> x.T.flat[3]
5
>>> type(x.flat)
<class 'numpy.flatiter'>
An assignment example:
>>> x.flat = 3; x
array([[3, 3, 3],
 [3, 3, 3]])
>>> x.flat[[1,4]] = 1; x
array([[3, 1, 3],
 [3, 1, 3]])

flatten(*args, **kwargs)¶

a.flatten(order=’C’)

Return a copy of the array collapsed into one dimension.

order{‘C’, ‘F’, ‘A’, ‘K’}, optional
‘C’ means to flatten in row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’.

yndarray
A copy of the input array, flattened to one dimension.

ravel : Return a flattened array. flat : A 1-D flat iterator over the array.
>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4])

getfield(*args, **kwargs)¶

a.getfield(dtype, offset=0)

Returns a field of the given array as a certain type.

A field is a view of the array data with a given data-type. The values in the view are determined by the given type and the offset into the current array in bytes. The offset needs to be such that the view dtype fits in the array dtype; for example an array of dtype complex128 has 16-byte elements. If taking a view with a 32-bit integer (4 bytes), the offset needs to be between 0 and 12 bytes.

dtypestr or dtype
The data type of the view. The dtype size of the view can not be larger than that of the array itself.

offsetint
Number of bytes to skip before beginning the element view.
>>> x = np.diag([1.+1.j]*2)
>>> x[1, 1] = 2 + 4.j
>>> x
array([[1.+1.j,  0.+0.j],
       [0.+0.j,  2.+4.j]])
>>> x.getfield(np.float64)
array([[1.,  0.],
       [0.,  2.]])
By choosing an offset of 8 bytes we can select the complex part of the array for our view:
>>> x.getfield(np.float64, offset=8)
array([[1.,  0.],
       [0.,  4.]])

imag(*args, **kwargs)¶

The imaginary part of the array.

>>> x = np.sqrt([1+0j, 0+1j])
>>> x.imag
array([ 0.        ,  0.70710678])
>>> x.imag.dtype
dtype('float64')

item(*args, **kwargs)¶

a.item(*args)

Copy an element of an array to a standard Python scalar and return it.

*args : Arguments (variable number and type)

none: in this case, the method only works for arrays with one element (a.size == 1), which element is copied into a standard Python scalar object and returned.

int_type: this argument is interpreted as a flat index into the array, specifying which element to copy and return.

tuple of int_types: functions as does a single int_type argument, except that the argument is interpreted as an nd-index into the array.

zStandard Python scalar object
A copy of the specified element of the array as a suitable Python scalar

When the data type of a is longdouble or clongdouble, item() returns a scalar array object because there is no available Python scalar that would not lose information. Void arrays return a buffer object for item(), unless fields are defined, in which case a tuple is returned.

item is very similar to a[args], except, instead of an array scalar, a standard Python scalar is returned. This can be useful for speeding up access to elements of the array and doing arithmetic on elements of the array using Python’s optimized math.
>>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
       [1, 3, 6],
       [1, 0, 1]])
>>> x.item(3)
1
>>> x.item(7)
0
>>> x.item((0, 1))
2
>>> x.item((2, 2))
1

itemset(*args, **kwargs)¶

a.itemset(*args)

Insert scalar into an array (scalar is cast to array’s dtype, if possible)

There must be at least 1 argument, and define the last argument as item. Then, a.itemset(*args) is equivalent to but faster than a[args] = item. The item should be a scalar value and args must select a single item in the array a.

*argsArguments
If one argument: a scalar, only used in case a is of size 1. If two arguments: the last argument is the value to be set and must be a scalar, the first argument specifies a single array element location. It is either an int or a tuple.

Compared to indexing syntax, itemset provides some speed increase for placing a scalar into a particular location in an ndarray, if you must do this. However, generally this is discouraged: among other problems, it complicates the appearance of the code. Also, when using itemset (and item) inside a loop, be sure to assign the methods to a local variable to avoid the attribute look-up at each loop iteration.
>>> np.random.seed(123)
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[2, 2, 6],
       [1, 3, 6],
       [1, 0, 1]])
>>> x.itemset(4, 0)
>>> x.itemset((2, 2), 9)
>>> x
array([[2, 2, 6],
       [1, 0, 6],
       [1, 0, 9]])

itemsize(*args, **kwargs)¶

Length of one array element in bytes.

>>> x = np.array([1,2,3], dtype=np.float64)
>>> x.itemsize
8
>>> x = np.array([1,2,3], dtype=np.complex128)
>>> x.itemsize
16

max(*args, **kwargs)¶: a.max(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

Return the maximum along a given axis.

Refer to numpy.amax for full documentation.

numpy.amax : equivalent function

mean(*args, **kwargs)¶: a.mean(axis=None, dtype=None, out=None, keepdims=False, *, where=True)

Returns the average of the array elements along given axis.

Refer to numpy.mean for full documentation.

numpy.mean : equivalent function

min(*args, **kwargs)¶: a.min(axis=None, out=None, keepdims=False, initial=<no value>, where=True)

Return the minimum along a given axis.

Refer to numpy.amin for full documentation.

numpy.amin : equivalent function

nbytes(*args, **kwargs)¶

Total bytes consumed by the elements of the array.

Does not include memory consumed by non-element attributes of the array object.

sys.getsizeof
Memory consumed by the object itself without parents in case view. This does include memory consumed by non-element attributes.
>>> x = np.zeros((3,5,2), dtype=np.complex128)
>>> x.nbytes
480
>>> np.prod(x.shape) * x.itemsize
480

ndim(*args, **kwargs)¶

Number of array dimensions.

>>> x = np.array([1, 2, 3])
>>> x.ndim
1
>>> y = np.zeros((2, 3, 4))
>>> y.ndim
3

newbyteorder(*args, **kwargs)¶

arr.newbyteorder(new_order=’S’, /)

Return the array with the same data viewed with a different byte order.

Equivalent to:
arr.view(arr.dtype.newbytorder(new_order))
Changes are also made in all fields and sub-arrays of the array data type.

new_orderstring, optional
Byte order to force; a value from the byte order specifications below. new_order codes can be any of:

‘S’ - swap dtype from current to opposite endian

{‘<’, ‘little’} - little endian

{‘>’, ‘big’} - big endian

{‘=’, ‘native’} - native order, equivalent to sys.byteorder

{‘|’, ‘I’} - ignore (no change to byte order)

The default value (‘S’) results in swapping the current byte order.

new_arrarray
New array object with the dtype reflecting given change to the byte order.

nonzero(*args, **kwargs)¶: a.nonzero()

Return the indices of the elements that are non-zero.

Refer to numpy.nonzero for full documentation.

numpy.nonzero : equivalent function

partition(*args, **kwargs)¶

a.partition(kth, axis=-1, kind=’introselect’, order=None)

Rearranges the elements in the array in such a way that the value of the element in kth position is in the position it would be in a sorted array. All elements smaller than the kth element are moved before this element and all equal or greater are moved behind it. The ordering of the elements in the two partitions is undefined.

Added in version 1.8.0.

kthint or sequence of ints
Element index to partition by. The kth element value will be in its final sorted position and all smaller elements will be moved before it and all equal or greater elements behind it. The order of all elements in the partitions is undefined. If provided with a sequence of kth it will partition all elements indexed by kth of them into their sorted position at once.

Deprecated since version 1.22.0: Passing booleans as index is deprecated.

axisint, optional
Axis along which to sort. Default is -1, which means sort along the last axis.

kind{‘introselect’}, optional
Selection algorithm. Default is ‘introselect’.

orderstr or list of str, optional
When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need to be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.

numpy.partition : Return a partitioned copy of an array. argpartition : Indirect partition. sort : Full sort.

See np.partition for notes on the different algorithms.
>>> a = np.array([3, 4, 2, 1])
>>> a.partition(3)
>>> a
array([2, 1, 3, 4])
>>> a.partition((1, 3))
>>> a
array([1, 2, 3, 4])

prod(*args, **kwargs)¶: a.prod(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)

Return the product of the array elements over the given axis

Refer to numpy.prod for full documentation.

numpy.prod : equivalent function

ptp(*args, **kwargs)¶: a.ptp(axis=None, out=None, keepdims=False)

Peak to peak (maximum - minimum) value along a given axis.

Refer to numpy.ptp for full documentation.

numpy.ptp : equivalent function

put(*args, **kwargs)¶: a.put(indices, values, mode=’raise’)

Set a.flat[n] = values[n] for all n in indices.

Refer to numpy.put for full documentation.

numpy.put : equivalent function

ravel(*args, **kwargs)¶: a.ravel([order])

Return a flattened array.

Refer to numpy.ravel for full documentation.

numpy.ravel : equivalent function

ndarray.flat : a flat iterator on the array.

real(*args, **kwargs)¶

The real part of the array.

>>> x = np.sqrt([1+0j, 0+1j])
>>> x.real
array([ 1.        ,  0.70710678])
>>> x.real.dtype
dtype('float64')

numpy.real : equivalent function

repeat(*args, **kwargs)¶: a.repeat(repeats, axis=None)

Repeat elements of an array.

Refer to numpy.repeat for full documentation.

numpy.repeat : equivalent function

reshape(*args, **kwargs)¶: a.reshape(shape, order=’C’)

Returns an array containing the same data with a new shape.

Refer to numpy.reshape for full documentation.

numpy.reshape : equivalent function

Unlike the free function numpy.reshape, this method on ndarray allows the elements of the shape parameter to be passed in as separate arguments. For example, a.reshape(10, 11) is equivalent to a.reshape((10, 11)).

resize(*args, **kwargs)¶

a.resize(new_shape, refcheck=True)

Change shape and size of array in-place.

new_shapetuple of ints, or n ints
Shape of resized array.

refcheckbool, optional
If False, reference count will not be checked. Default is True.

None

ValueError
If a does not own its own data or references or views to it exist, and the data memory must be changed. PyPy only: will always raise if the data memory must be changed, since there is no reliable way to determine if references or views to it exist.

SystemError
If the order keyword argument is specified. This behaviour is a bug in NumPy.

resize : Return a new array with the specified shape.

This reallocates space for the data area if necessary.

Only contiguous arrays (data elements consecutive in memory) can be resized.

The purpose of the reference count check is to make sure you do not use this array as a buffer for another Python object and then reallocate the memory. However, reference counts can increase in other ways so if you are sure that you have not shared the memory for this array with another Python object, then you may safely set refcheck to False.

Shrinking an array: array is flattened (in the order that the data are stored in memory), resized, and reshaped:
>>> a = np.array([[0, 1], [2, 3]], order='C')
>>> a.resize((2, 1))
>>> a
array([[0],
       [1]])
>>> a = np.array([[0, 1], [2, 3]], order='F')
>>> a.resize((2, 1))
>>> a
array([[0],
       [2]])
Enlarging an array: as above, but missing entries are filled with zeros:
>>> b = np.array([[0, 1], [2, 3]])
>>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
>>> b
array([[0, 1, 2],
       [3, 0, 0]])
Referencing an array prevents resizing…
>>> c = a
>>> a.resize((1, 1))
Traceback (most recent call last):
...
ValueError: cannot resize an array that references or is referenced ...
Unless refcheck is False:
>>> a.resize((1, 1), refcheck=False)
>>> a
array([[0]])
>>> c
array([[0]])

round(*args, **kwargs)¶: a.round(decimals=0, out=None)

Return a with each element rounded to the given number of decimals.

Refer to numpy.around for full documentation.

numpy.around : equivalent function

searchsorted(*args, **kwargs)¶: a.searchsorted(v, side=’left’, sorter=None)

Find indices where elements of v should be inserted in a to maintain order.

For full documentation, see numpy.searchsorted

numpy.searchsorted : equivalent function

setfield(*args, **kwargs)¶

a.setfield(val, dtype, offset=0)

Put a value into a specified place in a field defined by a data-type.

Place val into a’s field defined by dtype and beginning offset bytes into the field.

valobject
Value to be placed in field.

dtypedtype object
Data-type of the field in which to place val.

offsetint, optional
The number of bytes into the field at which to place val.

None

getfield
>>> x = np.eye(3)
>>> x.getfield(np.float64)
array([[1.,  0.,  0.],
       [0.,  1.,  0.],
       [0.,  0.,  1.]])
>>> x.setfield(3, np.int32)
>>> x.getfield(np.int32)
array([[3, 3, 3],
       [3, 3, 3],
       [3, 3, 3]], dtype=int32)
>>> x
array([[1.0e+000, 1.5e-323, 1.5e-323],
       [1.5e-323, 1.0e+000, 1.5e-323],
       [1.5e-323, 1.5e-323, 1.0e+000]])
>>> x.setfield(np.eye(3), np.int32)
>>> x
array([[1.,  0.,  0.],
       [0.,  1.,  0.],
       [0.,  0.,  1.]])

setflags(*args, **kwargs)¶

a.setflags(write=None, align=None, uic=None)

Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, respectively.

These Boolean-valued flags affect how numpy interprets the memory area used by a (see Notes below). The ALIGNED flag can only be set to True if the data is actually aligned according to the type. The WRITEBACKIFCOPY and flag can never be set to True. The flag WRITEABLE can only be set to True if the array owns its own memory, or the ultimate owner of the memory exposes a writeable buffer interface, or is a string. (The exception for string is made so that unpickling can be done without copying memory.)

writebool, optional
Describes whether or not a can be written to.

alignbool, optional
Describes whether or not a is aligned properly for its type.

uicbool, optional
Describes whether or not a is a copy of another “base” array.

Array flags provide information about how the memory area used for the array is to be interpreted. There are 7 Boolean flags in use, only four of which can be changed by the user: WRITEBACKIFCOPY, WRITEABLE, and ALIGNED.

WRITEABLE (W) the data area can be written to;

ALIGNED (A) the data and strides are aligned appropriately for the hardware (as determined by the compiler);

WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced by .base). When the C-API function PyArray_ResolveWritebackIfCopy is called, the base array will be updated with the contents of this array.

All flags can be accessed using the single (upper case) letter as well as the full name.
>>> y = np.array([[3, 1, 7],
... [2, 0, 0],
... [8, 5, 9]])
>>> y
array([[3, 1, 7],
 [2, 0, 0],
 [8, 5, 9]])
>>> y.flags
 C_CONTIGUOUS : True
 F_CONTIGUOUS : False
 OWNDATA : True
 WRITEABLE : True
 ALIGNED : True
 WRITEBACKIFCOPY : False
>>> y.setflags(write=0, align=0)
>>> y.flags
 C_CONTIGUOUS : True
 F_CONTIGUOUS : False
 OWNDATA : True
 WRITEABLE : False
 ALIGNED : False
 WRITEBACKIFCOPY : False
>>> y.setflags(uic=1)
Traceback (most recent call last):
 File "<stdin>", line 1, in <module>
ValueError: cannot set WRITEBACKIFCOPY flag to True

shape(*args, **kwargs)¶

Tuple of array dimensions.

The shape property is usually used to get the current shape of an array, but may also be used to reshape the array in-place by assigning a tuple of array dimensions to it. As with numpy.reshape, one of the new shape dimensions can be -1, in which case its value is inferred from the size of the array and the remaining dimensions. Reshaping an array in-place will fail if a copy is required.

Warning

Setting arr.shape is discouraged and may be deprecated in the future. Using ndarray.reshape is the preferred approach.
>>> x = np.array([1, 2, 3, 4])
>>> x.shape
(4,)
>>> y = np.zeros((2, 3, 4))
>>> y.shape
(2, 3, 4)
>>> y.shape = (3, 8)
>>> y
array([[ 0., 0., 0., 0., 0., 0., 0., 0.],
 [ 0., 0., 0., 0., 0., 0., 0., 0.],
 [ 0., 0., 0., 0., 0., 0., 0., 0.]])
>>> y.shape = (3, 6)
Traceback (most recent call last):
 File "<stdin>", line 1, in <module>
ValueError: total size of new array must be unchanged
>>> np.zeros((4,2))[::2].shape = (-1,)
Traceback (most recent call last):
 File "<stdin>", line 1, in <module>
AttributeError: Incompatible shape for in-place modification. Use
`.reshape()` to make a copy with the desired shape.
numpy.shape : Equivalent getter function. numpy.reshape : Function similar to setting shape. ndarray.reshape : Method similar to setting shape.

size(*args, **kwargs)¶

Number of elements in the array.

Equal to np.prod(a.shape), i.e., the product of the array’s dimensions.

a.size returns a standard arbitrary precision Python integer. This may not be the case with other methods of obtaining the same value (like the suggested np.prod(a.shape), which returns an instance of np.int_), and may be relevant if the value is used further in calculations that may overflow a fixed size integer type.
>>> x = np.zeros((3, 5, 2), dtype=np.complex128)
>>> x.size
30
>>> np.prod(x.shape)
30

sort(*args, **kwargs)¶

a.sort(axis=-1, kind=None, order=None)

Sort an array in-place. Refer to numpy.sort for full documentation.

axisint, optional
Axis along which to sort. Default is -1, which means sort along the last axis.

kind{‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, optional
Sorting algorithm. The default is ‘quicksort’. Note that both ‘stable’ and ‘mergesort’ use timsort under the covers and, in general, the actual implementation will vary with datatype. The ‘mergesort’ option is retained for backwards compatibility.

Changed in version 1.15.0: The ‘stable’ option was added.

orderstr or list of str, optional
When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.

numpy.sort : Return a sorted copy of an array. numpy.argsort : Indirect sort. numpy.lexsort : Indirect stable sort on multiple keys. numpy.searchsorted : Find elements in sorted array. numpy.partition: Partial sort.

See numpy.sort for notes on the different sorting algorithms.
>>> a = np.array([[1,4], [3,1]])
>>> a.sort(axis=1)
>>> a
array([[1, 4],
 [1, 3]])
>>> a.sort(axis=0)
>>> a
array([[1, 3],
 [1, 4]])
Use the order keyword to specify a field to use when sorting a structured array:
>>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
>>> a.sort(order='y')
>>> a
array([(b'c', 1), (b'a', 2)],
 dtype=[('x', 'S1'), ('y', '<i8')])

squeeze(*args, **kwargs)¶: a.squeeze(axis=None)

Remove axes of length one from a.

Refer to numpy.squeeze for full documentation.

numpy.squeeze : equivalent function

std(*args, **kwargs)¶: a.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

Returns the standard deviation of the array elements along given axis.

Refer to numpy.std for full documentation.

numpy.std : equivalent function

strides(*args, **kwargs)¶

Tuple of bytes to step in each dimension when traversing an array.

The byte offset of element (i[0], i[1], ..., i[n]) in an array a is:
offset = sum(np.array(i) * a.strides)
A more detailed explanation of strides can be found in the “ndarray.rst” file in the NumPy reference guide.

Warning

Setting arr.strides is discouraged and may be deprecated in the future. numpy.lib.stride_tricks.as_strided should be preferred to create a new view of the same data in a safer way.

Imagine an array of 32-bit integers (each 4 bytes):
x = np.array([[0, 1, 2, 3, 4],
              [5, 6, 7, 8, 9]], dtype=np.int32)
This array is stored in memory as 40 bytes, one after the other (known as a contiguous block of memory). The strides of an array tell us how many bytes we have to skip in memory to move to the next position along a certain axis. For example, we have to skip 4 bytes (1 value) to move to the next column, but 20 bytes (5 values) to get to the same position in the next row. As such, the strides for the array x will be (20, 4).

numpy.lib.stride_tricks.as_strided
>>> y = np.reshape(np.arange(2*3*4), (2,3,4))
>>> y
array([[[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]],
       [[12, 13, 14, 15],
        [16, 17, 18, 19],
        [20, 21, 22, 23]]])
>>> y.strides
(48, 16, 4)
>>> y[1,1,1]
17
>>> offset=sum(y.strides * np.array((1,1,1)))
>>> offset/y.itemsize
17
>>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)
>>> x.strides
(32, 4, 224, 1344)
>>> i = np.array([3,5,2,2])
>>> offset = sum(i * x.strides)
>>> x[3,5,2,2]
813
>>> offset / x.itemsize
813

sum(*args, **kwargs)¶: a.sum(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)

Return the sum of the array elements over the given axis.

Refer to numpy.sum for full documentation.

numpy.sum : equivalent function

swapaxes(*args, **kwargs)¶: a.swapaxes(axis1, axis2)

Return a view of the array with axis1 and axis2 interchanged.

Refer to numpy.swapaxes for full documentation.

numpy.swapaxes : equivalent function

take(*args, **kwargs)¶: a.take(indices, axis=None, out=None, mode=’raise’)

Return an array formed from the elements of a at the given indices.

Refer to numpy.take for full documentation.

numpy.take : equivalent function

tobytes(*args, **kwargs)¶

a.tobytes(order=’C’)

Construct Python bytes containing the raw data bytes in the array.

Constructs Python bytes showing a copy of the raw contents of data memory. The bytes object is produced in C-order by default. This behavior is controlled by the order parameter.

Added in version 1.9.0.

order{‘C’, ‘F’, ‘A’}, optional
Controls the memory layout of the bytes object. ‘C’ means C-order, ‘F’ means F-order, ‘A’ (short for Any) means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. Default is ‘C’.

sbytes
Python bytes exhibiting a copy of a’s raw data.

frombuffer
Inverse of this operation, construct a 1-dimensional array from Python bytes.
>>> x = np.array([[0, 1], [2, 3]], dtype='<u2')
>>> x.tobytes()
b'\x00\x00\x01\x00\x02\x00\x03\x00'
>>> x.tobytes('C') == x.tobytes()
True
>>> x.tobytes('F')
b'\x00\x00\x02\x00\x01\x00\x03\x00'

tofile(*args, **kwargs)¶

a.tofile(fid, sep=””, format=”%s”)

Write array to a file as text or binary (default).

Data is always written in ‘C’ order, independent of the order of a. The data produced by this method can be recovered using the function fromfile().

fidfile or str or Path
An open file object, or a string containing a filename.

Changed in version 1.17.0: pathlib.Path objects are now accepted.

sepstr
Separator between array items for text output. If “” (empty), a binary file is written, equivalent to file.write(a.tobytes()).

formatstr
Format string for text file output. Each entry in the array is formatted to text by first converting it to the closest Python type, and then using “format” % item.

This is a convenience function for quick storage of array data. Information on endianness and precision is lost, so this method is not a good choice for files intended to archive data or transport data between machines with different endianness. Some of these problems can be overcome by outputting the data as text files, at the expense of speed and file size.

When fid is a file object, array contents are directly written to the file, bypassing the file object’s write method. As a result, tofile cannot be used with files objects supporting compression (e.g., GzipFile) or file-like objects that do not support fileno() (e.g., BytesIO).

tolist(*args, **kwargs)¶

a.tolist()

Return the array as an a.ndim-levels deep nested list of Python scalars.

Return a copy of the array data as a (nested) Python list. Data items are converted to the nearest compatible builtin Python type, via the ~numpy.ndarray.item function.

If a.ndim is 0, then since the depth of the nested list is 0, it will not be a list at all, but a simple Python scalar.

none

yobject, or list of object, or list of list of object, or …
The possibly nested list of array elements.

The array may be recreated via a = np.array(a.tolist()), although this may sometimes lose precision.

For a 1D array, a.tolist() is almost the same as list(a), except that tolist changes numpy scalars to Python scalars:
>>> a = np.uint32([1, 2])
>>> a_list = list(a)
>>> a_list
[1, 2]
>>> type(a_list[0])
<class 'numpy.uint32'>
>>> a_tolist = a.tolist()
>>> a_tolist
[1, 2]
>>> type(a_tolist[0])
<class 'int'>
Additionally, for a 2D array, tolist applies recursively:
>>> a = np.array([[1, 2], [3, 4]])
>>> list(a)
[array([1, 2]), array([3, 4])]
>>> a.tolist()
[[1, 2], [3, 4]]
The base case for this recursion is a 0D array:
>>> a = np.array(1)
>>> list(a)
Traceback (most recent call last):
 ...
TypeError: iteration over a 0-d array
>>> a.tolist()
1

tostring(*args, **kwargs)¶: a.tostring(order=’C’)

A compatibility alias for tobytes, with exactly the same behavior.

Despite its name, it returns bytes not strs.

Deprecated since version 1.19.0.

trace(*args, **kwargs)¶: a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)

Return the sum along diagonals of the array.

Refer to numpy.trace for full documentation.

numpy.trace : equivalent function

transpose(*args, **kwargs)¶

a.transpose(*axes)

Returns a view of the array with axes transposed.

Refer to numpy.transpose for full documentation.

axes : None, tuple of ints, or n ints

None or no argument: reverses the order of the axes.

tuple of ints: i in the j-th place in the tuple means that the array’s i-th axis becomes the transposed array’s j-th axis.

n ints: same as an n-tuple of the same ints (this form is intended simply as a “convenience” alternative to the tuple form).

pndarray
View of the array with its axes suitably permuted.

transpose : Equivalent function. ndarray.T : Array property returning the array transposed. ndarray.reshape : Give a new shape to an array without changing its data.
>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
       [3, 4]])
>>> a.transpose()
array([[1, 3],
       [2, 4]])
>>> a.transpose((1, 0))
array([[1, 3],
       [2, 4]])
>>> a.transpose(1, 0)
array([[1, 3],
       [2, 4]])
>>> a = np.array([1, 2, 3, 4])
>>> a
array([1, 2, 3, 4])
>>> a.transpose()
array([1, 2, 3, 4])

var(*args, **kwargs)¶: a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)

Returns the variance of the array elements, along given axis.

Refer to numpy.var for full documentation.

numpy.var : equivalent function

view(*args, **kwargs)¶

a.view([dtype][, type])

New view of array with the same data.

Note

Passing None for dtype is different from omitting the parameter, since the former invokes dtype(None) which is an alias for dtype('float_').

dtypedata-type or ndarray sub-class, optional
Data-type descriptor of the returned view, e.g., float32 or int16. Omitting it results in the view having the same data-type as a. This argument can also be specified as an ndarray sub-class, which then specifies the type of the returned object (this is equivalent to setting the type parameter).

typePython type, optional
Type of the returned view, e.g., ndarray or matrix. Again, omission of the parameter results in type preservation.

a.view() is used two different ways:

a.view(some_dtype) or a.view(dtype=some_dtype) constructs a view of the array’s memory with a different data-type. This can cause a reinterpretation of the bytes of memory.

a.view(ndarray_subclass) or a.view(type=ndarray_subclass) just returns an instance of ndarray_subclass that looks at the same array (same shape, dtype, etc.) This does not cause a reinterpretation of the memory.

For a.view(some_dtype), if some_dtype has a different number of bytes per entry than the previous dtype (for example, converting a regular array to a structured array), then the last axis of a must be contiguous. This axis will be resized in the result.

Changed in version 1.23.0: Only the last axis needs to be contiguous. Previously, the entire array had to be C-contiguous.
>>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
Viewing array data using a different type and dtype:
>>> y = x.view(dtype=np.int16, type=np.matrix)
>>> y
matrix([[513]], dtype=int16)
>>> print(type(y))
<class 'numpy.matrix'>
Creating a view on a structured array so it can be used in calculations
>>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
>>> xv = x.view(dtype=np.int8).reshape(-1,2)
>>> xv
array([[1, 2],
 [3, 4]], dtype=int8)
>>> xv.mean(0)
array([2., 3.])
Making changes to the view changes the underlying array
>>> xv[0,1] = 20
>>> x
array([(1, 20), (3, 4)], dtype=[('a', 'i1'), ('b', 'i1')])
Using a view to convert an array to a recarray:
>>> z = x.view(np.recarray)
>>> z.a
array([1, 3], dtype=int8)
Views share data:
>>> x[0] = (9, 10)
>>> z[0]
(9, 10)
Views that change the dtype size (bytes per entry) should normally be avoided on arrays defined by slices, transposes, fortran-ordering, etc.:
>>> x = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int16)
>>> y = x[:, ::2]
>>> y
array([[1, 3],
 [4, 6]], dtype=int16)
>>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
Traceback (most recent call last):
 ...
ValueError: To change to a dtype of a different size, the last axis must be contiguous
>>> z = y.copy()
>>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
array([[(1, 3)],
 [(4, 6)]], dtype=[('width', '<i2'), ('length', '<i2')])
However, views that change dtype are totally fine for arrays with a contiguous last axis, even if the rest of the axes are not C-contiguous:
>>> x = np.arange(2 * 3 * 4, dtype=np.int8).reshape(2, 3, 4)
>>> x.transpose(1, 0, 2).view(np.int16)
array([[[ 256, 770],
 [3340, 3854]],

 [[1284, 1798],
 [4368, 4882]],

 [[2312, 2826],
 [5396, 5910]]], dtype=int16)

arkouda.numeric_and_bool_scalars¶

arkouda.numeric_scalars¶

arkouda.numpy_scalars¶

Create a pdarray filled with ones.

Parameters:

size (int_scalars or tuple of int_scalars) – Size or shape of the array
dtype (Union[float64, int64, bool]) – Resulting array type, default ak.float64
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays Included for consistency, as ones are all zeros ending on a one, regardless of max_bits

Returns:

Ones of the requested size or shape and dtype

Return type:

Raises:

TypeError – Raised if the supplied dtype is not supported
RuntimeError – Raised if the size parameter is neither an int nor a str that is parseable to an int.
ValueError – Raised if the rank of the given shape is not in get_array_ranks() or is empty

See also

zeros, ones_like

Examples

>>> ak.ones(5, dtype=ak.int64)
array([1 1 1 1 1])

>>> ak.ones(5, dtype=ak.float64)
array([1.00000000000000000 1.00000000000000000 1.00000000000000000
       1.00000000000000000 1.00000000000000000])

>>> ak.ones(5, dtype=ak.bool_)
array([True True True True True])

Notes

Logic for generating the pdarray is delegated to the ak.full method.

Create a pdarray filled with ones.

Parameters:

size (int_scalars or tuple of int_scalars) – Size or shape of the array
dtype (Union[float64, int64, bool]) – Resulting array type, default ak.float64
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays Included for consistency, as ones are all zeros ending on a one, regardless of max_bits

Returns:

Ones of the requested size or shape and dtype

Return type:

Raises:

TypeError – Raised if the supplied dtype is not supported
RuntimeError – Raised if the size parameter is neither an int nor a str that is parseable to an int.
ValueError – Raised if the rank of the given shape is not in get_array_ranks() or is empty

See also

zeros, ones_like

Examples

>>> ak.ones(5, dtype=ak.int64)
array([1 1 1 1 1])

>>> ak.ones(5, dtype=ak.float64)
array([1.00000000000000000 1.00000000000000000 1.00000000000000000
       1.00000000000000000 1.00000000000000000])

>>> ak.ones(5, dtype=ak.bool_)
array([True True True True True])

Notes

Logic for generating the pdarray is delegated to the ak.full method.

arkouda.ones_like(pda: arkouda.numpy.pdarrayclass.pdarray) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Create a one-filled pdarray of the same size and dtype as an existing pdarray.

Parameters:: pda (pdarray) – Array to use for size and dtype
Returns:: Equivalent to ak.ones(pda.size, pda.dtype)
Return type:: pdarray
Raises:: TypeError – Raised if the pda parameter is not a pdarray.

See also

ones, zeros_like

Notes

Logic for generating the pdarray is delegated to the ak.ones method. Accordingly, the supported dtypes match are defined by the ak.ones method.

Examples

>>> ak.ones_like(ak.zeros(5,dtype=ak.int64))
array([1 1 1 1 1])

>>> ak.ones_like(ak.zeros(5,dtype=ak.float64))
array([1.00000000000000000 1.00000000000000000 1.00000000000000000
       1.00000000000000000 1.00000000000000000])

>>> ak.ones_like(ak.zeros(5,dtype=ak.bool_))
array([True True True True True])

arkouda.parity(pda: pdarray) → pdarray[source]¶

Find the bit parity (XOR of all bits) for each integer in an array.

Parameters:: pda (pdarray, int64, uint64, bigint) – Input array (must be integral).
Returns:: parity – The parity of each element: 0 if even number of bits set, 1 if odd.
Return type:: pdarray
Raises:: TypeError – If input array is not int64, uint64, or bigint

Examples

>>> A = ak.arange(10)
>>> ak.parity(A)
array([0, 1, 1, 0, 1, 0, 0, 1, 1, 0])

class arkouda.pdarray[source]¶

The basic arkouda array class. This class contains only the attributes of the array; the data resides on the arkouda server. When a server operation results in a new array, arkouda will create a pdarray instance that points to the array data on the server. As such, the user should not initialize pdarray instances directly.

name¶

The server-side identifier for the array

Type:: str

dtype¶

The element type of the array

Type:: dtype

size¶

The number of elements in the array

Type:: int_scalars

ndim¶

The rank of the array

Type:: int_scalars

shape¶

A list or tuple containing the sizes of each dimension of the array

Type:: Sequence[int]

itemsize¶

The size in bytes of each element

Type:: int_scalars

BinOps(*args, **kwargs)¶

frozenset() -> empty frozenset object frozenset(iterable) -> frozenset object

Build an immutable unordered collection of unique elements.

OpEqOps(*args, **kwargs)¶

frozenset() -> empty frozenset object frozenset(iterable) -> frozenset object

Build an immutable unordered collection of unique elements.

all(axis: int | Tuple[int, ...] | None = None, keepdims: bool = False) → bool_scalars | pdarray[source]¶

Return True iff all elements of the array along the given axis evaluate to True.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

boolean if axis is omitted, pdarray if axis is supplied

Return type:

boolean or pdarray

Examples

>>> ak.all(ak.array([True,False,False]))
False
>>> ak.all(ak.array([[True,True,False],[False,True,True]]),axis=0)
array([False True False])
>>> ak.all(ak.array([[True,True,True],[False,False,False]]),axis=0,keepdims=True)
array([array([False False False])])
>>> ak.all(ak.array([[True,True,True],[False,False,False]]),axis=1,keepdims=True)
array([array([True]) array([False])])
>>> ak.array([True,False,False]).all()
False

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Notes

Works as a method of a pdarray (e.g. a.any()) or a standalone function (e.g. ak.all(a))

any(axis: int | Tuple[int, ...] | None = None, keepdims: bool = False) → bool_scalars | pdarray[source]¶

Return True iff any element of the array along the given axis evaluates to True.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

boolean if axis is omitted, else pdarray if axis is supplied

Return type:

boolean or pdarray

Examples

>>> ak.any(ak.array([True,False,False]))
True
>>> ak.any(ak.array([[True,True,False],[False,True,True]]),axis=0)
array([True True True])
>>> ak.any(ak.array([[True,True,True],[False,False,False]]),axis=0,keepdims=True)
array([array([True True True])])
>>> ak.any(ak.array([[True,True,True],[False,False,False]]),axis=1,keepdims=True)
array([array([True]) array([False])])
>>> ak.array([True,False,False]).any()
True

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Notes

Works as a method of a pdarray (e.g. a.any()) or a standalone function (e.g. ak.any(a))

argmax(axis: int | None | None = None, keepdims: bool = False) → np.int64 | np.uint64 | pdarray[source]¶

Return index of the first occurrence of the maximum along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

int64 or uint64 if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

int64, uint64 or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.argmax(ak.array([1,2,3,4,5]))
4
>>> ak.argmax(ak.array([5.5,4.5,3.5,2.5,1.5]))
0
>>> ak.array([[1,2,3],[5,4,3]]).argmax(axis=1)
array([2 0])

Notes

Works as a method of a pdarray (e.g. a.argmax()) or a standalone function (e.g. ak.argmax(a))

argmaxk(k: int_scalars) → pdarray[source]¶: Finds the indices corresponding to the k maximum values of an array. See arkouda.argmaxk for details.

argmin(axis: int | None | None = None, keepdims: bool = False) → np.int64 | np.uint64 | pdarray[source]¶

Return index of the first occurrence of the minimum along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

int64 or uint64 if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

int64, uint64 or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.argmin(ak.array([1,2,3,4,5]))
0
>>> ak.argmin(ak.array([5.5,4.5,3.5,2.5,1.5]))
4
>>> ak.array([[1,2,3],[5,4,3]]).argmin(axis=1)
array([0 2])

Notes

Works as a method of a pdarray (e.g. a.argmin()) or a standalone function (e.g. ak.argmin(a))

argmink(k: int_scalars) → pdarray[source]¶: Finds the indices corresponding to the k minimum values of an array. See arkouda.argmink for details.

astype(dtype) → pdarray[source]¶

Cast values of pdarray to provided dtype

Parameters:: dtype (np.dtype or str) – Dtype to cast to

Examples

>>> ak.array([1,2,3]).astype(ak.float64)
array([1.00000000000000000 2.00000000000000000 3.00000000000000000])
>>> ak.array([1.5,2.5]).astype(ak.int64)
array([1 2])
>>> ak.array([True,False]).astype(ak.int64)
array([1 0])

Returns:: An arkouda pdarray with values converted to the specified data type
Return type:: ak.pdarray

Notes

This is essentially shorthand for ak.cast(x, ‘<dtype>’) where x is a pdarray.

attach(user_defined_name: str) → pdarray[source]¶

class method to return a pdarray attached to the registered name in the arkouda server which was registered using register()

Parameters:: user_defined_name (str) – user defined name which array was registered under
Returns:: pdarray which is bound to the corresponding server side component which was registered with user_defined_name
Return type:: pdarray
Raises:: TypeError – Raised if user_defined_name is not a str

Notes

Registered names/pdarrays in the server are immune to deletion until they are unregistered.

Examples

>>> a = zeros(100)
>>> a.register("my_zeros")
>>> # potentially disconnect from server and reconnect to server
>>> b = ak.pdarray.attach("my_zeros")
>>> # ...other work...
>>> b.unregister()

bigint_to_uint_arrays() → List[pdarray][source]¶

Creates a list of uint pdarrays from a bigint pdarray. The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.

Returns:: A list of uint pdarrays where: The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.
Return type:: List[pdarrays]
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> a = ak.arange(2**64, 2**64 + 5)
>>> a
array([18446744073709551616 18446744073709551617 18446744073709551618
18446744073709551619 18446744073709551620])
>>> a.bigint_to_uint_arrays()
[array([1 1 1 1 1]), array([0 1 2 3 4])]

clz() → pdarray[source]¶: Count the number of leading zeros in each element. See ak.clz.

corr(y: pdarray) → np.float64[source]¶: Compute the correlation between self and y using pearson correlation coefficient. See arkouda.corr for details.

cov(y: pdarray) → np.float64[source]¶: Compute the covariance between self and y.

ctz() → pdarray[source]¶: Count the number of trailing zeros in each element. See ak.ctz.

equals(other) → bool_scalars[source]¶

Whether pdarrays are the same size and all entries are equal.

Parameters:: other (object) – object to compare.
Returns:: True if the pdarrays are the same, o.w. False.
Return type:: bool

Examples

>>> a = ak.array([1, 2, 3])
>>> a_cpy = ak.array([1, 2, 3])
>>> a.equals(a_cpy)
True
>>> a2 = ak.array([1, 2, 5)
>>> a.equals(a2)
False

fill(value: numeric_scalars) → None[source]¶

Fill the array (in place) with a constant value.

Parameters:: value (numeric_scalars)
Raises:: TypeError – Raised if value is not an int, int64, float, or float64

flatten()[source]¶

Return a copy of the array collapsed into one dimension.

Return type:: A copy of the input array, flattened to one dimension.

Examples

>>> a = ak.array([[3,2,1],[2,3,1]])
>>> a.flatten()
array([3 2 1 2 3 1])

format_other(other) → str[source]¶

Attempt to cast scalar other to the element dtype of this pdarray, and print the resulting value to a string (e.g. for sending to a server command). The user should not call this function directly.

Parameters:: other (object) – The scalar to be cast to the pdarray.dtype
Return type:: string representation of np.dtype corresponding to the other parameter
Raises:: TypeError – Raised if the other parameter cannot be converted to Numpy dtype

property inferred_type: str¶: Return a string of the type inferred from the values.

info() → str[source]¶

Returns a JSON formatted string containing information about all components of self

Parameters:: None
Returns:: JSON string containing information about all components of self
Return type:: str

is_registered() → np.bool_[source]¶

Return True iff the object is contained in the registry

Parameters:: None
Returns:: Indicates if the object is contained in the registry
Return type:: bool
Raises:: RuntimeError – Raised if there’s a server-side error thrown

Note

This will return True if the object is registered itself or as a component of another object

is_sorted(axis: int | Tuple[int, ...] | None = None, keepdims: bool = False) → bool_scalars | pdarray[source]¶

Return True iff the array (or given axis of the array) is monotonically non-decreasing.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

boolean if axis is omitted, else pdarray if axis is supplied

Return type:

boolean or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.is_sorted(ak.array([1,2,3,4,5]))
True
>>> ak.is_sorted(ak.array([5,4,3,2,1]))
False
>>> ak.array([[1,2,3],[5,4,3]]).is_sorted(axis=1)
array([True False])

Notes

Works as a method of a pdarray (e.g. a.is_sorted()) or a standalone function (e.g. ak.is_sorted(a))

max(axis: int | Tuple[int, ...] | None = None, keepdims: bool = False) → numpy_scalars | pdarray[source]¶

Return max of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numpy_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numpy_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.max(ak.array([1,2,3,4,5]))
5
>>> ak.max(ak.array([5.5,4.5,3.5,2.5,1.5]))
5.5
>>> ak.array([[1,2,3],[5,4,3]]).max(axis=1)
array([3 5])

Notes

Works as a method of a pdarray (e.g. a.max()) or a standalone function (e.g. ak.max(a))

property max_bits: numpy_scalars | pdarray¶

maxk(k: int_scalars) → pdarray[source]¶: Compute the maximum “k” values. See arkouda.maxk for details.

mean() → np.float64[source]¶: Compute the mean. See arkouda.mean for details.

min(axis: int | Tuple[int, ...] | None = None, keepdims: bool = False) → numpy_scalars | pdarray[source]¶

Return min of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numpy_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numpy_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.min(ak.array([1,2,3,4,5]))
1
>>> ak.min(ak.array([5.5,4.5,3.5,2.5,1.5]))
1.5
>>> ak.array([[1,2,3],[5,4,3]]).min(axis=1)
array([1 3])

Notes

Works as a method of a pdarray (e.g. a.min()) or a standalone function (e.g. ak.min(a))

mink(k: int_scalars) → pdarray[source]¶: Compute the minimum “k” values. See arkouda.mink for details.

property nbytes: pdarray¶

The size of the pdarray in bytes.

Returns:: The size of the pdarray in bytes.
Return type:: int

objType(*args, **kwargs)¶

str(object=’’) -> str str(bytes_or_buffer[, encoding[, errors]]) -> str

opeq(other, op)[source]¶

parity() → pdarray[source]¶: Find the parity (XOR of all bits) in each element. See ak.parity.

popcount() → pdarray[source]¶: Find the population (number of bits set) in each element. See ak.popcount.

pretty_print_info() → None[source]¶

Prints information about all components of self in a human readable format

Parameters:: None
Return type:: None

prod(axis: int | Tuple[int, ...] | None = None, keepdims: bool = False) → numpy_scalars | pdarray[source]¶

Return prod of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, defalt = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numpy_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numpy_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.prod(ak.array([1,2,3,4,5]))
120
>>> ak.prod(ak.array([5.5,4.5,3.5,2.5,1.5]))
324.84375
>>> ak.array([[1,2,3],[5,4,3]]).prod(axis=1)
array([6 60])

Notes

Works as a method of a pdarray (e.g. a.prod()) or a standalone function (e.g. ak.prod(a))

register(user_defined_name: str) → pdarray[source]¶

Register this pdarray with a user defined name in the arkouda server so it can be attached to later using pdarray.attach() This is an in-place operation, registering a pdarray more than once will update the name in the registry and remove the previously registered name. A name can only be registered to one pdarray at a time.

Parameters:

user_defined_name (str) – user defined name array is to be registered under

Returns:

The same pdarray which is now registered with the arkouda server and has an updated name. This is an in-place modification, the original is returned to support a fluid programming style. Please note you cannot register two different pdarrays with the same name.

Return type:

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the pdarray with the user_defined_name If the user is attempting to register more than one pdarray with the same name, the former should be unregistered first to free up the registration name.

Notes

Registered names/pdarrays in the server are immune to deletion until they are unregistered.

Examples

>>> a = zeros(100)
>>> a.register("my_zeros")
>>> # potentially disconnect from server and reconnect to server
>>> b = ak.pdarray.attach("my_zeros")
>>> # ...other work...
>>> b.unregister()

reshape(*shape)[source]¶

Gives a new shape to an array without changing its data.

Parameters:: shape (int, tuple of ints, or pdarray) – The new shape should be compatible with the original shape.
Returns:: a pdarray with the same data, reshaped to the new shape
Return type:: pdarray

Examples

>>> a = ak.array([[3,2,1],[2,3,1]])
>>> a.reshape((3,2))
array([array([3 2]) array([1 2]) array([3 1])])
>>> a.reshape(3,2)
array([array([3 2]) array([1 2]) array([3 1])])
>>> a.reshape((6,1))
array([array([3]) array([2]) array([1]) array([2]) array([3]) array([1])])

Notes

only available as a method, not as a standalone function, i.e., a.reshape(compatibleShape) is valid, but ak.reshape(a,compatibleShape) is not.

rotl(other) → pdarray[source]¶: Rotate bits left by <other>.

rotr(other) → pdarray[source]¶: Rotate bits right by <other>.

save(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None, file_format: str = 'HDF5', file_type: str = 'distribute') → str[source]¶

DEPRECATED Save the pdarray to HDF5 or Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. HDF5 support single files, in which case the file name will only be that provided. Each locale saves its chunk of the array to its corresponding file.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files (must not already exist)
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, attempt to create new dataset in existing files.
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
file_format (str {'HDF5', 'Parquet'}) – By default, saved files will be written to the HDF5 file format. If ‘Parquet’, the files will be written to the Parquet file format. This is case insensitive.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.

Return type:

string message indicating result of save operation

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray
ValueError – Raised if there is an error in parsing the prefix path pointing to file write location or if the mode parameter is neither truncate nor append
TypeError – Raised if any one of the prefix_path, dataset, or mode parameters is not a string

Notes

The prefix_path must be visible to the arkouda server and the user must have write permission. Output files have names of the form <prefix_path>_LOCALE, where  ranges from 0 to numLocales. If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, a RuntimeError will result. Previously all files saved in Parquet format were saved with a .parquet file extension. This will require you to use load as if you saved the file with the extension. Try this if an older file is not being found. Any file extension can be used.The file I/O does not rely on the extension to determine the file format.

Examples

>>> a = ak.arange(25)
>>> # Saving without an extension
>>> a.save('path/prefix', dataset='array')
Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####``
>>> # Saving with an extension (HDF5)
>>> a.save('path/prefix.h5', dataset='array')
Saves the array to numLocales HDF5 files with the name
``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number
>>> # Saving with an extension (Parquet)
>>> a.save('path/prefix.parquet', dataset='array', file_format='Parquet')
Saves the array in numLocales Parquet files with the name
``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number

property shape: str¶

Return the shape of an array.

Returns:: The elements of the shape tuple give the lengths of the corresponding array dimensions.
Return type:: tuple of int

slice_bits(low, high) → pdarray[source]¶

Returns a pdarray containing only bits from low to high of self.

This is zero indexed and inclusive on both ends, so slicing the bottom 64 bits is pda.slice_bits(0, 63)

Parameters:

low (int) – The lowest bit included in the slice (inclusive) zero indexed, so the first bit is 0
high (int) – The highest bit included in the slice (inclusive)

Returns:

A new pdarray containing the bits of self from low to high

Return type:

Raises:

RuntimeError – Raised if there is a server-side error thrown

Examples

>>> p = ak.array([2**65 + (2**64 - 1)])
>>> bin(p[0])
'0b101111111111111111111111111111111111111111111111111111111111111111'
>>> bin(p.slice_bits(64, 65)[0])
'0b10'
>>> a = ak.array([143,15])
>>> a.slice_bits(1,3)
array([7 7])
>>> a.slice_bits(4,9)
array([8 0])
>>> a.slice_bits(1,9)
array([71 7])

std(ddof: int_scalars = 0) → np.float64[source]¶: Compute the standard deviation. See arkouda.std for details.

sum(axis: int | Tuple[int, ...] | None = None, keepdims: bool = False) → numpy_scalars | pdarray[source]¶

Return sum of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numpy_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numpy_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.sum(ak.array([1,2,3,4,5]))
15
>>> ak.sum(ak.array([5.5,4.5,3.5,2.5,1.5]))
17.5
>>> ak.array([[1,2,3],[5,4,3]]).sum(axis=1)
array([6 12])

Notes

Works as a method of a pdarray (e.g. a.sum()) or a standalone function (e.g. ak.sum(a))

to_csv(prefix_path: str, dataset: str = 'array', col_delim: str = ',', overwrite: bool = False)[source]¶

Write pdarry to CSV file(s). File will contain a single column with the pdarray data. All CSV files written by Arkouda include a header denoting data types of the columns.

Parameters:

prefix_path (str) – filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
dataset (str, defaults to "array") – column name to save the pdarray under.
col_delim (str, defaults to ",") – value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
overwrite (bool, defaults to False) – If True, existing files matching the provided path will be overwritten. if False and existing files are found, an error will be returned.

Returns:

response message

Return type:

str

Raises:

ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist
RuntimeError – Raised if one or more of the specified files cannot be opened. if ‘allow_errors’ is true, this may be raised if no values are returned from the server.
TypeError – Raise if the server returns an unknown arkouda_type

Notes

CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for all column names and data
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (”n”) at this time.

to_cuda()[source]¶

Convert the array to a Numba DeviceND array, transferring array data from the arkouda server to Python via ndarray. If the array exceeds a builtin size limit, a RuntimeError is raised.

Returns:

A Numba ndarray with the same attributes and data as the pdarray; on GPU

Return type:

numba.DeviceNDArray

Raises:

ImportError – Raised if CUDA is not available
ModuleNotFoundError – Raised if Numba is either not installed or not enabled
RuntimeError – Raised if there is a server-side error thrown in the course of retrieving the pdarray.

Notes

The number of bytes in the array cannot exceed client.maxTransferBytes, otherwise a RuntimeError will be raised. This is to protect the user from overflowing the memory of the system on which the Python client is running, under the assumption that the server is running on a distributed system with much more memory than the client. The user may override this limit by setting client.maxTransferBytes to a larger value, but proceed with caution.

See also

array

Examples

>>> a = ak.arange(0, 5, 1)
>>> a.to_cuda()
array([0, 1, 2, 3, 4])
>>> type(a.to_cuda())
numpy.devicendarray

to_hdf(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', file_type: str = 'distribute') → str[source]¶

Save the pdarray to HDF5. The object can be saved to a collection of files or single file.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files (must not already exist)
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, attempt to create new dataset in existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.

Return type:

string message indicating result of save operation

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

The prefix_path must be visible to the arkouda server and the user must

Examples

>>> a = ak.arange(25)
>>> # Saving without an extension
>>> a.to_hdf('path/prefix', dataset='array')
Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####``
>>> # Saving with an extension (HDF5)
>>> a.to_hdf('path/prefix.h5', dataset='array')
Saves the array to numLocales HDF5 files with the name
``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number
>>> # Saving to a single file
>>> a.to_hdf('path/prefix.hdf5', dataset='array', file_type='single')
Saves the array in to single hdf5 file on the root node.
``cwd/path/name_prefix.hdf5``

to_list() → List[source]¶

Convert the array to a list, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.

Returns:: A list with the same data as the pdarray
Return type:: list
Raises:: RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes

Notes

See also

Examples

>>> a = ak.arange(0, 5, 1)
>>> a.to_list()
[0, 1, 2, 3, 4]
>>> type(a.to_list())
<class 'list'>

to_ndarray() → np.ndarray[source]¶

Convert the array to a np.ndarray, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.

Returns:: A numpy ndarray with the same attributes and data as the pdarray
Return type:: np.ndarray
Raises:: RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes

Notes

See also

Examples

>>> a = ak.arange(0, 5, 1)
>>> a.to_ndarray()
array([0, 1, 2, 3, 4])
>>> type(a.to_ndarray())
<class 'numpy.ndarray'>

to_parquet(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None) → str[source]¶

Save the pdarray to Parquet. The result is a collection of files, one file per locale of the arkouda server, where each filename starts with prefix_path. Each locale saves its chunk of the array to its corresponding file.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files (must not already exist)
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, attempt to create new dataset in existing files.
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files

Return type:

string message indicating result of save operation

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

The prefix_path must be visible to the arkouda server and the user must

Examples

>>> a = ak.arange(25)
>>> # Saving without an extension
>>> a.to_parquet('path/prefix', dataset='array')
Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####``
>>> # Saving with an extension (HDF5)
>>> a.to_parqet('path/prefix.parquet', dataset='array')
Saves the array to numLocales HDF5 files with the name
``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number

transfer(hostname: str, port: int_scalars)[source]¶

Sends a pdarray to a different Arkouda server

Parameters:

hostname (str) – The hostname where the Arkouda server intended to receive the pdarray is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().

Return type:

A message indicating a complete transfer

Raises:

ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype

unregister() → None[source]¶

Unregister a pdarray in the arkouda server which was previously registered using register() and/or attahced to using attach()

Return type:: None
Raises:: RuntimeError – Raised if the server could not find the internal name/symbol to remove

Notes

Registered names/pdarrays in the server are immune to deletion until they are unregistered.

Examples

>>> a = zeros(100)
>>> a.register("my_zeros")
>>> # potentially disconnect from server and reconnect to server
>>> b = ak.pdarray.attach("my_zeros")
>>> # ...other work...
>>> b.unregister()

update_hdf(prefix_path: str, dataset: str = 'array', repack: bool = True)[source]¶

Overwrite the dataset with the name provided with this pdarray. If the dataset does not exist it is added

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.

Return type:

str - success message if successful

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added

value_counts()[source]¶

Count the occurrences of the unique values of self.

Returns:

unique_values (pdarray) – The unique values, sorted in ascending order
counts (pdarray, int64) – The number of times the corresponding unique value occurs

Examples

>>> ak.array([2, 0, 2, 4, 0, 0]).value_counts()
(array([0, 2, 4]), array([3, 2, 1]))

var(ddof: int_scalars = 0) → np.float64[source]¶: Compute the variance. See arkouda.var for details.

class arkouda.pdarray(name: str, mydtype: numpy.dtype | str, size: arkouda.numpy.dtypes.int_scalars, ndim: arkouda.numpy.dtypes.int_scalars, shape: Sequence[int], itemsize: arkouda.numpy.dtypes.int_scalars, max_bits: int | None = None)[source]¶

name¶

The server-side identifier for the array

Type:: str

dtype¶

The element type of the array

Type:: dtype

size¶

The number of elements in the array

Type:: int_scalars

ndim¶

The rank of the array

Type:: int_scalars

shape¶

A list or tuple containing the sizes of each dimension of the array

Type:: Sequence[int]

itemsize¶

The size in bytes of each element

Type:: int_scalars

BinOps¶

OpEqOps¶

all(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.bool_scalars | pdarray[source]¶

Return True iff all elements of the array along the given axis evaluate to True.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

boolean if axis is omitted, pdarray if axis is supplied

Return type:

boolean or pdarray

Examples

>>> ak.all(ak.array([True,False,False]))
False
>>> ak.all(ak.array([[True,True,False],[False,True,True]]),axis=0)
array([False True False])
>>> ak.all(ak.array([[True,True,True],[False,False,False]]),axis=0,keepdims=True)
array([array([False False False])])
>>> ak.all(ak.array([[True,True,True],[False,False,False]]),axis=1,keepdims=True)
array([array([True]) array([False])])
>>> ak.array([True,False,False]).all()
False

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Notes

Works as a method of a pdarray (e.g. a.any()) or a standalone function (e.g. ak.all(a))

any(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.bool_scalars | pdarray[source]¶

Return True iff any element of the array along the given axis evaluates to True.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

boolean if axis is omitted, else pdarray if axis is supplied

Return type:

boolean or pdarray

Examples

>>> ak.any(ak.array([True,False,False]))
True
>>> ak.any(ak.array([[True,True,False],[False,True,True]]),axis=0)
array([True True True])
>>> ak.any(ak.array([[True,True,True],[False,False,False]]),axis=0,keepdims=True)
array([array([True True True])])
>>> ak.any(ak.array([[True,True,True],[False,False,False]]),axis=1,keepdims=True)
array([array([True]) array([False])])
>>> ak.array([True,False,False]).any()
True

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Notes

Works as a method of a pdarray (e.g. a.any()) or a standalone function (e.g. ak.any(a))

argmax(axis: int | None | None = None, keepdims: bool = False) → numpy.int64 | numpy.uint64 | pdarray[source]¶

Return index of the first occurrence of the maximum along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

int64 or uint64 if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

int64, uint64 or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.argmax(ak.array([1,2,3,4,5]))
4
>>> ak.argmax(ak.array([5.5,4.5,3.5,2.5,1.5]))
0
>>> ak.array([[1,2,3],[5,4,3]]).argmax(axis=1)
array([2 0])

Notes

Works as a method of a pdarray (e.g. a.argmax()) or a standalone function (e.g. ak.argmax(a))

argmaxk(k: arkouda.numpy.dtypes.int_scalars) → pdarray[source]¶: Finds the indices corresponding to the k maximum values of an array. See arkouda.argmaxk for details.

argmin(axis: int | None | None = None, keepdims: bool = False) → numpy.int64 | numpy.uint64 | pdarray[source]¶

Return index of the first occurrence of the minimum along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

int64 or uint64 if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

int64, uint64 or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.argmin(ak.array([1,2,3,4,5]))
0
>>> ak.argmin(ak.array([5.5,4.5,3.5,2.5,1.5]))
4
>>> ak.array([[1,2,3],[5,4,3]]).argmin(axis=1)
array([0 2])

Notes

Works as a method of a pdarray (e.g. a.argmin()) or a standalone function (e.g. ak.argmin(a))

argmink(k: arkouda.numpy.dtypes.int_scalars) → pdarray[source]¶: Finds the indices corresponding to the k minimum values of an array. See arkouda.argmink for details.

astype(dtype) → pdarray[source]¶

Cast values of pdarray to provided dtype

Parameters:: dtype (np.dtype or str) – Dtype to cast to

Examples

>>> ak.array([1,2,3]).astype(ak.float64)
array([1.00000000000000000 2.00000000000000000 3.00000000000000000])
>>> ak.array([1.5,2.5]).astype(ak.int64)
array([1 2])
>>> ak.array([True,False]).astype(ak.int64)
array([1 0])

Returns:: An arkouda pdarray with values converted to the specified data type
Return type:: ak.pdarray

Notes

This is essentially shorthand for ak.cast(x, ‘<dtype>’) where x is a pdarray.

static attach(user_defined_name: str) → pdarray[source]¶

class method to return a pdarray attached to the registered name in the arkouda server which was registered using register()

Parameters:: user_defined_name (str) – user defined name which array was registered under
Returns:: pdarray which is bound to the corresponding server side component which was registered with user_defined_name
Return type:: pdarray
Raises:: TypeError – Raised if user_defined_name is not a str

Notes

Registered names/pdarrays in the server are immune to deletion until they are unregistered.

Examples

>>> a = zeros(100)
>>> a.register("my_zeros")
>>> # potentially disconnect from server and reconnect to server
>>> b = ak.pdarray.attach("my_zeros")
>>> # ...other work...
>>> b.unregister()

bigint_to_uint_arrays() → List[pdarray][source]¶

Creates a list of uint pdarrays from a bigint pdarray. The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.

Returns:: A list of uint pdarrays where: The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.
Return type:: List[pdarrays]
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> a = ak.arange(2**64, 2**64 + 5)
>>> a
array([18446744073709551616 18446744073709551617 18446744073709551618
18446744073709551619 18446744073709551620])
>>> a.bigint_to_uint_arrays()
[array([1 1 1 1 1]), array([0 1 2 3 4])]

clz() → pdarray[source]¶: Count the number of leading zeros in each element. See ak.clz.

corr(y: pdarray) → numpy.float64[source]¶: Compute the correlation between self and y using pearson correlation coefficient. See arkouda.corr for details.

cov(y: pdarray) → numpy.float64[source]¶: Compute the covariance between self and y.

ctz() → pdarray[source]¶: Count the number of trailing zeros in each element. See ak.ctz.

dtype¶

equals(other) → arkouda.numpy.dtypes.bool_scalars[source]¶

Whether pdarrays are the same size and all entries are equal.

Parameters:: other (object) – object to compare.
Returns:: True if the pdarrays are the same, o.w. False.
Return type:: bool

Examples

>>> a = ak.array([1, 2, 3])
>>> a_cpy = ak.array([1, 2, 3])
>>> a.equals(a_cpy)
True
>>> a2 = ak.array([1, 2, 5)
>>> a.equals(a2)
False

fill(value: arkouda.numpy.dtypes.numeric_scalars) → None[source]¶

Fill the array (in place) with a constant value.

Parameters:: value (numeric_scalars)
Raises:: TypeError – Raised if value is not an int, int64, float, or float64

flatten()[source]¶

Return a copy of the array collapsed into one dimension.

Return type:: A copy of the input array, flattened to one dimension.

Examples

>>> a = ak.array([[3,2,1],[2,3,1]])
>>> a.flatten()
array([3 2 1 2 3 1])

format_other(other) → str[source]¶

Attempt to cast scalar other to the element dtype of this pdarray, and print the resulting value to a string (e.g. for sending to a server command). The user should not call this function directly.

Parameters:: other (object) – The scalar to be cast to the pdarray.dtype
Return type:: string representation of np.dtype corresponding to the other parameter
Raises:: TypeError – Raised if the other parameter cannot be converted to Numpy dtype

property inferred_type: str | None¶: Return a string of the type inferred from the values.

info() → str[source]¶

Returns a JSON formatted string containing information about all components of self

Parameters:: None
Returns:: JSON string containing information about all components of self
Return type:: str

is_registered() → numpy.bool_[source]¶

Return True iff the object is contained in the registry

Parameters:: None
Returns:: Indicates if the object is contained in the registry
Return type:: bool
Raises:: RuntimeError – Raised if there’s a server-side error thrown

Note

This will return True if the object is registered itself or as a component of another object

is_sorted(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.bool_scalars | pdarray[source]¶

Return True iff the array (or given axis of the array) is monotonically non-decreasing.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

boolean if axis is omitted, else pdarray if axis is supplied

Return type:

boolean or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.is_sorted(ak.array([1,2,3,4,5]))
True
>>> ak.is_sorted(ak.array([5,4,3,2,1]))
False
>>> ak.array([[1,2,3],[5,4,3]]).is_sorted(axis=1)
array([True False])

Notes

Works as a method of a pdarray (e.g. a.is_sorted()) or a standalone function (e.g. ak.is_sorted(a))

itemsize¶

max(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.numpy_scalars | pdarray[source]¶

Return max of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numpy_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numpy_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.max(ak.array([1,2,3,4,5]))
5
>>> ak.max(ak.array([5.5,4.5,3.5,2.5,1.5]))
5.5
>>> ak.array([[1,2,3],[5,4,3]]).max(axis=1)
array([3 5])

Notes

Works as a method of a pdarray (e.g. a.max()) or a standalone function (e.g. ak.max(a))

property max_bits¶

maxk(k: arkouda.numpy.dtypes.int_scalars) → pdarray[source]¶: Compute the maximum “k” values. See arkouda.maxk for details.

mean() → numpy.float64[source]¶: Compute the mean. See arkouda.mean for details.

min(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.numpy_scalars | pdarray[source]¶

Return min of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numpy_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numpy_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.min(ak.array([1,2,3,4,5]))
1
>>> ak.min(ak.array([5.5,4.5,3.5,2.5,1.5]))
1.5
>>> ak.array([[1,2,3],[5,4,3]]).min(axis=1)
array([1 3])

Notes

Works as a method of a pdarray (e.g. a.min()) or a standalone function (e.g. ak.min(a))

mink(k: arkouda.numpy.dtypes.int_scalars) → pdarray[source]¶: Compute the minimum “k” values. See arkouda.mink for details.

name¶

property nbytes¶

The size of the pdarray in bytes.

Returns:: The size of the pdarray in bytes.
Return type:: int

ndim¶

objType = 'pdarray'¶

opeq(other, op)[source]¶

parity() → pdarray[source]¶: Find the parity (XOR of all bits) in each element. See ak.parity.

popcount() → pdarray[source]¶: Find the population (number of bits set) in each element. See ak.popcount.

pretty_print_info() → None[source]¶

Prints information about all components of self in a human readable format

Parameters:: None
Return type:: None

prod(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.numpy_scalars | pdarray[source]¶

Return prod of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, defalt = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numpy_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numpy_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.prod(ak.array([1,2,3,4,5]))
120
>>> ak.prod(ak.array([5.5,4.5,3.5,2.5,1.5]))
324.84375
>>> ak.array([[1,2,3],[5,4,3]]).prod(axis=1)
array([6 60])

Notes

Works as a method of a pdarray (e.g. a.prod()) or a standalone function (e.g. ak.prod(a))

register(user_defined_name: str) → pdarray[source]¶

Parameters:

user_defined_name (str) – user defined name array is to be registered under

Returns:

Return type:

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the pdarray with the user_defined_name If the user is attempting to register more than one pdarray with the same name, the former should be unregistered first to free up the registration name.

Notes

Registered names/pdarrays in the server are immune to deletion until they are unregistered.

Examples

>>> a = zeros(100)
>>> a.register("my_zeros")
>>> # potentially disconnect from server and reconnect to server
>>> b = ak.pdarray.attach("my_zeros")
>>> # ...other work...
>>> b.unregister()

registered_name: str | None = None¶

reshape(*shape)[source]¶

Gives a new shape to an array without changing its data.

Parameters:: shape (int, tuple of ints, or pdarray) – The new shape should be compatible with the original shape.
Returns:: a pdarray with the same data, reshaped to the new shape
Return type:: pdarray

Examples

>>> a = ak.array([[3,2,1],[2,3,1]])
>>> a.reshape((3,2))
array([array([3 2]) array([1 2]) array([3 1])])
>>> a.reshape(3,2)
array([array([3 2]) array([1 2]) array([3 1])])
>>> a.reshape((6,1))
array([array([3]) array([2]) array([1]) array([2]) array([3]) array([1])])

Notes

only available as a method, not as a standalone function, i.e., a.reshape(compatibleShape) is valid, but ak.reshape(a,compatibleShape) is not.

rotl(other) → pdarray[source]¶: Rotate bits left by <other>.

rotr(other) → pdarray[source]¶: Rotate bits right by <other>.

save(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None, file_format: str = 'HDF5', file_type: str = 'distribute') → str[source]¶

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files (must not already exist)
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, attempt to create new dataset in existing files.
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
file_format (str {'HDF5', 'Parquet'}) – By default, saved files will be written to the HDF5 file format. If ‘Parquet’, the files will be written to the Parquet file format. This is case insensitive.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.

Return type:

string message indicating result of save operation

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray
ValueError – Raised if there is an error in parsing the prefix path pointing to file write location or if the mode parameter is neither truncate nor append
TypeError – Raised if any one of the prefix_path, dataset, or mode parameters is not a string

Notes

The prefix_path must be visible to the arkouda server and the user must have write permission. Output files have names of the form <prefix_path>_LOCALE, where  ranges from 0 to numLocales. If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, a RuntimeError will result. Previously all files saved in Parquet format were saved with a .parquet file extension. This will require you to use load as if you saved the file with the extension. Try this if an older file is not being found. Any file extension can be used.The file I/O does not rely on the extension to determine the file format.

Examples

>>> a = ak.arange(25)
>>> # Saving without an extension
>>> a.save('path/prefix', dataset='array')
Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####``
>>> # Saving with an extension (HDF5)
>>> a.save('path/prefix.h5', dataset='array')
Saves the array to numLocales HDF5 files with the name
``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number
>>> # Saving with an extension (Parquet)
>>> a.save('path/prefix.parquet', dataset='array', file_format='Parquet')
Saves the array in numLocales Parquet files with the name
``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number

property shape¶

Return the shape of an array.

Returns:: The elements of the shape tuple give the lengths of the corresponding array dimensions.
Return type:: tuple of int

size¶

slice_bits(low, high) → pdarray[source]¶

Returns a pdarray containing only bits from low to high of self.

This is zero indexed and inclusive on both ends, so slicing the bottom 64 bits is pda.slice_bits(0, 63)

Parameters:

low (int) – The lowest bit included in the slice (inclusive) zero indexed, so the first bit is 0
high (int) – The highest bit included in the slice (inclusive)

Returns:

A new pdarray containing the bits of self from low to high

Return type:

Raises:

RuntimeError – Raised if there is a server-side error thrown

Examples

>>> p = ak.array([2**65 + (2**64 - 1)])
>>> bin(p[0])
'0b101111111111111111111111111111111111111111111111111111111111111111'
>>> bin(p.slice_bits(64, 65)[0])
'0b10'
>>> a = ak.array([143,15])
>>> a.slice_bits(1,3)
array([7 7])
>>> a.slice_bits(4,9)
array([8 0])
>>> a.slice_bits(1,9)
array([71 7])

std(ddof: arkouda.numpy.dtypes.int_scalars = 0) → numpy.float64[source]¶: Compute the standard deviation. See arkouda.std for details.

sum(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.numpy_scalars | pdarray[source]¶

Return sum of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numpy_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numpy_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.sum(ak.array([1,2,3,4,5]))
15
>>> ak.sum(ak.array([5.5,4.5,3.5,2.5,1.5]))
17.5
>>> ak.array([[1,2,3],[5,4,3]]).sum(axis=1)
array([6 12])

Notes

Works as a method of a pdarray (e.g. a.sum()) or a standalone function (e.g. ak.sum(a))

to_csv(prefix_path: str, dataset: str = 'array', col_delim: str = ',', overwrite: bool = False)[source]¶

Write pdarry to CSV file(s). File will contain a single column with the pdarray data. All CSV files written by Arkouda include a header denoting data types of the columns.

Parameters:

prefix_path (str) – filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
dataset (str, defaults to "array") – column name to save the pdarray under.
col_delim (str, defaults to ",") – value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
overwrite (bool, defaults to False) – If True, existing files matching the provided path will be overwritten. if False and existing files are found, an error will be returned.

Returns:

response message

Return type:

str

Raises:

ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist
RuntimeError – Raised if one or more of the specified files cannot be opened. if ‘allow_errors’ is true, this may be raised if no values are returned from the server.
TypeError – Raise if the server returns an unknown arkouda_type

Notes

CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for all column names and data
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (”n”) at this time.

to_cuda()[source]¶

Convert the array to a Numba DeviceND array, transferring array data from the arkouda server to Python via ndarray. If the array exceeds a builtin size limit, a RuntimeError is raised.

Returns:

A Numba ndarray with the same attributes and data as the pdarray; on GPU

Return type:

numba.DeviceNDArray

Raises:

ImportError – Raised if CUDA is not available
ModuleNotFoundError – Raised if Numba is either not installed or not enabled
RuntimeError – Raised if there is a server-side error thrown in the course of retrieving the pdarray.

Notes

See also

array

Examples

>>> a = ak.arange(0, 5, 1)
>>> a.to_cuda()
array([0, 1, 2, 3, 4])
>>> type(a.to_cuda())
numpy.devicendarray

to_hdf(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', file_type: str = 'distribute') → str[source]¶

Save the pdarray to HDF5. The object can be saved to a collection of files or single file.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files (must not already exist)
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, attempt to create new dataset in existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.

Return type:

string message indicating result of save operation

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

The prefix_path must be visible to the arkouda server and the user must

Examples

>>> a = ak.arange(25)
>>> # Saving without an extension
>>> a.to_hdf('path/prefix', dataset='array')
Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####``
>>> # Saving with an extension (HDF5)
>>> a.to_hdf('path/prefix.h5', dataset='array')
Saves the array to numLocales HDF5 files with the name
``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number
>>> # Saving to a single file
>>> a.to_hdf('path/prefix.hdf5', dataset='array', file_type='single')
Saves the array in to single hdf5 file on the root node.
``cwd/path/name_prefix.hdf5``

to_list() → List[source]¶

Convert the array to a list, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.

Returns:: A list with the same data as the pdarray
Return type:: list
Raises:: RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes

Notes

See also

Examples

>>> a = ak.arange(0, 5, 1)
>>> a.to_list()
[0, 1, 2, 3, 4]
>>> type(a.to_list())
<class 'list'>

to_ndarray() → numpy.ndarray[source]¶

Convert the array to a np.ndarray, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.

Returns:: A numpy ndarray with the same attributes and data as the pdarray
Return type:: np.ndarray
Raises:: RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes

Notes

See also

Examples

>>> a = ak.arange(0, 5, 1)
>>> a.to_ndarray()
array([0, 1, 2, 3, 4])
>>> type(a.to_ndarray())
<class 'numpy.ndarray'>

to_parquet(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None) → str[source]¶

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files (must not already exist)
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, attempt to create new dataset in existing files.
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files

Return type:

string message indicating result of save operation

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

The prefix_path must be visible to the arkouda server and the user must

Examples

>>> a = ak.arange(25)
>>> # Saving without an extension
>>> a.to_parquet('path/prefix', dataset='array')
Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####``
>>> # Saving with an extension (HDF5)
>>> a.to_parqet('path/prefix.parquet', dataset='array')
Saves the array to numLocales HDF5 files with the name
``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number

transfer(hostname: str, port: arkouda.numpy.dtypes.int_scalars)[source]¶

Sends a pdarray to a different Arkouda server

Parameters:

hostname (str) – The hostname where the Arkouda server intended to receive the pdarray is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().

Return type:

A message indicating a complete transfer

Raises:

ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype

unregister() → None[source]¶

Unregister a pdarray in the arkouda server which was previously registered using register() and/or attahced to using attach()

Return type:: None
Raises:: RuntimeError – Raised if the server could not find the internal name/symbol to remove

Notes

Registered names/pdarrays in the server are immune to deletion until they are unregistered.

Examples

>>> a = zeros(100)
>>> a.register("my_zeros")
>>> # potentially disconnect from server and reconnect to server
>>> b = ak.pdarray.attach("my_zeros")
>>> # ...other work...
>>> b.unregister()

update_hdf(prefix_path: str, dataset: str = 'array', repack: bool = True)[source]¶

Overwrite the dataset with the name provided with this pdarray. If the dataset does not exist it is added

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.

Return type:

str - success message if successful

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added

value_counts()[source]¶

Count the occurrences of the unique values of self.

Returns:

unique_values (pdarray) – The unique values, sorted in ascending order
counts (pdarray, int64) – The number of times the corresponding unique value occurs

Examples

>>> ak.array([2, 0, 2, 4, 0, 0]).value_counts()
(array([0, 2, 4]), array([3, 2, 1]))

var(ddof: arkouda.numpy.dtypes.int_scalars = 0) → numpy.float64[source]¶: Compute the variance. See arkouda.var for details.

name¶

The server-side identifier for the array

Type:: str

dtype¶

The element type of the array

Type:: dtype

size¶

The number of elements in the array

Type:: int_scalars

ndim¶

The rank of the array

Type:: int_scalars

shape¶

A list or tuple containing the sizes of each dimension of the array

Type:: Sequence[int]

itemsize¶

The size in bytes of each element

Type:: int_scalars

BinOps¶

OpEqOps¶

all(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.bool_scalars | pdarray[source]¶

Return True iff all elements of the array along the given axis evaluate to True.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

boolean if axis is omitted, pdarray if axis is supplied

Return type:

boolean or pdarray

Examples

>>> ak.all(ak.array([True,False,False]))
False
>>> ak.all(ak.array([[True,True,False],[False,True,True]]),axis=0)
array([False True False])
>>> ak.all(ak.array([[True,True,True],[False,False,False]]),axis=0,keepdims=True)
array([array([False False False])])
>>> ak.all(ak.array([[True,True,True],[False,False,False]]),axis=1,keepdims=True)
array([array([True]) array([False])])
>>> ak.array([True,False,False]).all()
False

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Notes

Works as a method of a pdarray (e.g. a.any()) or a standalone function (e.g. ak.all(a))

any(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.bool_scalars | pdarray[source]¶

Return True iff any element of the array along the given axis evaluates to True.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

boolean if axis is omitted, else pdarray if axis is supplied

Return type:

boolean or pdarray

Examples

>>> ak.any(ak.array([True,False,False]))
True
>>> ak.any(ak.array([[True,True,False],[False,True,True]]),axis=0)
array([True True True])
>>> ak.any(ak.array([[True,True,True],[False,False,False]]),axis=0,keepdims=True)
array([array([True True True])])
>>> ak.any(ak.array([[True,True,True],[False,False,False]]),axis=1,keepdims=True)
array([array([True]) array([False])])
>>> ak.array([True,False,False]).any()
True

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Notes

Works as a method of a pdarray (e.g. a.any()) or a standalone function (e.g. ak.any(a))

argmax(axis: int | None | None = None, keepdims: bool = False) → numpy.int64 | numpy.uint64 | pdarray[source]¶

Return index of the first occurrence of the maximum along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

int64 or uint64 if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

int64, uint64 or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.argmax(ak.array([1,2,3,4,5]))
4
>>> ak.argmax(ak.array([5.5,4.5,3.5,2.5,1.5]))
0
>>> ak.array([[1,2,3],[5,4,3]]).argmax(axis=1)
array([2 0])

Notes

Works as a method of a pdarray (e.g. a.argmax()) or a standalone function (e.g. ak.argmax(a))

argmaxk(k: arkouda.numpy.dtypes.int_scalars) → pdarray[source]¶: Finds the indices corresponding to the k maximum values of an array. See arkouda.argmaxk for details.

argmin(axis: int | None | None = None, keepdims: bool = False) → numpy.int64 | numpy.uint64 | pdarray[source]¶

Return index of the first occurrence of the minimum along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

int64 or uint64 if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

int64, uint64 or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.argmin(ak.array([1,2,3,4,5]))
0
>>> ak.argmin(ak.array([5.5,4.5,3.5,2.5,1.5]))
4
>>> ak.array([[1,2,3],[5,4,3]]).argmin(axis=1)
array([0 2])

Notes

Works as a method of a pdarray (e.g. a.argmin()) or a standalone function (e.g. ak.argmin(a))

argmink(k: arkouda.numpy.dtypes.int_scalars) → pdarray[source]¶: Finds the indices corresponding to the k minimum values of an array. See arkouda.argmink for details.

astype(dtype) → pdarray[source]¶

Cast values of pdarray to provided dtype

Parameters:: dtype (np.dtype or str) – Dtype to cast to

Examples

>>> ak.array([1,2,3]).astype(ak.float64)
array([1.00000000000000000 2.00000000000000000 3.00000000000000000])
>>> ak.array([1.5,2.5]).astype(ak.int64)
array([1 2])
>>> ak.array([True,False]).astype(ak.int64)
array([1 0])

Returns:: An arkouda pdarray with values converted to the specified data type
Return type:: ak.pdarray

Notes

This is essentially shorthand for ak.cast(x, ‘<dtype>’) where x is a pdarray.

static attach(user_defined_name: str) → pdarray[source]¶

class method to return a pdarray attached to the registered name in the arkouda server which was registered using register()

Parameters:: user_defined_name (str) – user defined name which array was registered under
Returns:: pdarray which is bound to the corresponding server side component which was registered with user_defined_name
Return type:: pdarray
Raises:: TypeError – Raised if user_defined_name is not a str

Notes

Registered names/pdarrays in the server are immune to deletion until they are unregistered.

Examples

>>> a = zeros(100)
>>> a.register("my_zeros")
>>> # potentially disconnect from server and reconnect to server
>>> b = ak.pdarray.attach("my_zeros")
>>> # ...other work...
>>> b.unregister()

bigint_to_uint_arrays() → List[pdarray][source]¶

Creates a list of uint pdarrays from a bigint pdarray. The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.

Returns:: A list of uint pdarrays where: The first item in return will be the highest 64 bits of the bigint pdarray and the last item will be the lowest 64 bits.
Return type:: List[pdarrays]
Raises:: RuntimeError – Raised if there is a server-side error thrown

Examples

>>> a = ak.arange(2**64, 2**64 + 5)
>>> a
array([18446744073709551616 18446744073709551617 18446744073709551618
18446744073709551619 18446744073709551620])
>>> a.bigint_to_uint_arrays()
[array([1 1 1 1 1]), array([0 1 2 3 4])]

clz() → pdarray[source]¶: Count the number of leading zeros in each element. See ak.clz.

corr(y: pdarray) → numpy.float64[source]¶: Compute the correlation between self and y using pearson correlation coefficient. See arkouda.corr for details.

cov(y: pdarray) → numpy.float64[source]¶: Compute the covariance between self and y.

ctz() → pdarray[source]¶: Count the number of trailing zeros in each element. See ak.ctz.

dtype¶

equals(other) → arkouda.numpy.dtypes.bool_scalars[source]¶

Whether pdarrays are the same size and all entries are equal.

Parameters:: other (object) – object to compare.
Returns:: True if the pdarrays are the same, o.w. False.
Return type:: bool

Examples

>>> a = ak.array([1, 2, 3])
>>> a_cpy = ak.array([1, 2, 3])
>>> a.equals(a_cpy)
True
>>> a2 = ak.array([1, 2, 5)
>>> a.equals(a2)
False

fill(value: arkouda.numpy.dtypes.numeric_scalars) → None[source]¶

Fill the array (in place) with a constant value.

Parameters:: value (numeric_scalars)
Raises:: TypeError – Raised if value is not an int, int64, float, or float64

flatten()[source]¶

Return a copy of the array collapsed into one dimension.

Return type:: A copy of the input array, flattened to one dimension.

Examples

>>> a = ak.array([[3,2,1],[2,3,1]])
>>> a.flatten()
array([3 2 1 2 3 1])

format_other(other) → str[source]¶

Attempt to cast scalar other to the element dtype of this pdarray, and print the resulting value to a string (e.g. for sending to a server command). The user should not call this function directly.

Parameters:: other (object) – The scalar to be cast to the pdarray.dtype
Return type:: string representation of np.dtype corresponding to the other parameter
Raises:: TypeError – Raised if the other parameter cannot be converted to Numpy dtype

property inferred_type: str | None¶: Return a string of the type inferred from the values.

info() → str[source]¶

Returns a JSON formatted string containing information about all components of self

Parameters:: None
Returns:: JSON string containing information about all components of self
Return type:: str

is_registered() → numpy.bool_[source]¶

Return True iff the object is contained in the registry

Parameters:: None
Returns:: Indicates if the object is contained in the registry
Return type:: bool
Raises:: RuntimeError – Raised if there’s a server-side error thrown

Note

This will return True if the object is registered itself or as a component of another object

is_sorted(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.bool_scalars | pdarray[source]¶

Return True iff the array (or given axis of the array) is monotonically non-decreasing.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

boolean if axis is omitted, else pdarray if axis is supplied

Return type:

boolean or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.is_sorted(ak.array([1,2,3,4,5]))
True
>>> ak.is_sorted(ak.array([5,4,3,2,1]))
False
>>> ak.array([[1,2,3],[5,4,3]]).is_sorted(axis=1)
array([True False])

Notes

Works as a method of a pdarray (e.g. a.is_sorted()) or a standalone function (e.g. ak.is_sorted(a))

itemsize¶

max(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.numpy_scalars | pdarray[source]¶

Return max of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numpy_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numpy_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.max(ak.array([1,2,3,4,5]))
5
>>> ak.max(ak.array([5.5,4.5,3.5,2.5,1.5]))
5.5
>>> ak.array([[1,2,3],[5,4,3]]).max(axis=1)
array([3 5])

Notes

Works as a method of a pdarray (e.g. a.max()) or a standalone function (e.g. ak.max(a))

property max_bits¶

maxk(k: arkouda.numpy.dtypes.int_scalars) → pdarray[source]¶: Compute the maximum “k” values. See arkouda.maxk for details.

mean() → numpy.float64[source]¶: Compute the mean. See arkouda.mean for details.

min(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.numpy_scalars | pdarray[source]¶

Return min of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numpy_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numpy_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.min(ak.array([1,2,3,4,5]))
1
>>> ak.min(ak.array([5.5,4.5,3.5,2.5,1.5]))
1.5
>>> ak.array([[1,2,3],[5,4,3]]).min(axis=1)
array([1 3])

Notes

Works as a method of a pdarray (e.g. a.min()) or a standalone function (e.g. ak.min(a))

mink(k: arkouda.numpy.dtypes.int_scalars) → pdarray[source]¶: Compute the minimum “k” values. See arkouda.mink for details.

name¶

property nbytes¶

The size of the pdarray in bytes.

Returns:: The size of the pdarray in bytes.
Return type:: int

ndim¶

objType = 'pdarray'¶

opeq(other, op)[source]¶

parity() → pdarray[source]¶: Find the parity (XOR of all bits) in each element. See ak.parity.

popcount() → pdarray[source]¶: Find the population (number of bits set) in each element. See ak.popcount.

pretty_print_info() → None[source]¶

Prints information about all components of self in a human readable format

Parameters:: None
Return type:: None

prod(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.numpy_scalars | pdarray[source]¶

Return prod of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, defalt = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numpy_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numpy_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.prod(ak.array([1,2,3,4,5]))
120
>>> ak.prod(ak.array([5.5,4.5,3.5,2.5,1.5]))
324.84375
>>> ak.array([[1,2,3],[5,4,3]]).prod(axis=1)
array([6 60])

Notes

Works as a method of a pdarray (e.g. a.prod()) or a standalone function (e.g. ak.prod(a))

register(user_defined_name: str) → pdarray[source]¶

Parameters:

user_defined_name (str) – user defined name array is to be registered under

Returns:

Return type:

Raises:

TypeError – Raised if user_defined_name is not a str
RegistrationError – If the server was unable to register the pdarray with the user_defined_name If the user is attempting to register more than one pdarray with the same name, the former should be unregistered first to free up the registration name.

Notes

Registered names/pdarrays in the server are immune to deletion until they are unregistered.

Examples

>>> a = zeros(100)
>>> a.register("my_zeros")
>>> # potentially disconnect from server and reconnect to server
>>> b = ak.pdarray.attach("my_zeros")
>>> # ...other work...
>>> b.unregister()

registered_name: str | None = None¶

reshape(*shape)[source]¶

Gives a new shape to an array without changing its data.

Parameters:: shape (int, tuple of ints, or pdarray) – The new shape should be compatible with the original shape.
Returns:: a pdarray with the same data, reshaped to the new shape
Return type:: pdarray

Examples

>>> a = ak.array([[3,2,1],[2,3,1]])
>>> a.reshape((3,2))
array([array([3 2]) array([1 2]) array([3 1])])
>>> a.reshape(3,2)
array([array([3 2]) array([1 2]) array([3 1])])
>>> a.reshape((6,1))
array([array([3]) array([2]) array([1]) array([2]) array([3]) array([1])])

Notes

only available as a method, not as a standalone function, i.e., a.reshape(compatibleShape) is valid, but ak.reshape(a,compatibleShape) is not.

rotl(other) → pdarray[source]¶: Rotate bits left by <other>.

rotr(other) → pdarray[source]¶: Rotate bits right by <other>.

save(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None, file_format: str = 'HDF5', file_type: str = 'distribute') → str[source]¶

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files (must not already exist)
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, attempt to create new dataset in existing files.
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files
file_format (str {'HDF5', 'Parquet'}) – By default, saved files will be written to the HDF5 file format. If ‘Parquet’, the files will be written to the Parquet file format. This is case insensitive.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.

Return type:

string message indicating result of save operation

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray
ValueError – Raised if there is an error in parsing the prefix path pointing to file write location or if the mode parameter is neither truncate nor append
TypeError – Raised if any one of the prefix_path, dataset, or mode parameters is not a string

Notes

The prefix_path must be visible to the arkouda server and the user must have write permission. Output files have names of the form <prefix_path>_LOCALE, where  ranges from 0 to numLocales. If any of the output files already exist and the mode is ‘truncate’, they will be overwritten. If the mode is ‘append’ and the number of output files is less than the number of locales or a dataset with the same name already exists, a RuntimeError will result. Previously all files saved in Parquet format were saved with a .parquet file extension. This will require you to use load as if you saved the file with the extension. Try this if an older file is not being found. Any file extension can be used.The file I/O does not rely on the extension to determine the file format.

Examples

>>> a = ak.arange(25)
>>> # Saving without an extension
>>> a.save('path/prefix', dataset='array')
Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####``
>>> # Saving with an extension (HDF5)
>>> a.save('path/prefix.h5', dataset='array')
Saves the array to numLocales HDF5 files with the name
``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number
>>> # Saving with an extension (Parquet)
>>> a.save('path/prefix.parquet', dataset='array', file_format='Parquet')
Saves the array in numLocales Parquet files with the name
``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number

property shape¶

Return the shape of an array.

Returns:: The elements of the shape tuple give the lengths of the corresponding array dimensions.
Return type:: tuple of int

size¶

slice_bits(low, high) → pdarray[source]¶

Returns a pdarray containing only bits from low to high of self.

This is zero indexed and inclusive on both ends, so slicing the bottom 64 bits is pda.slice_bits(0, 63)

Parameters:

low (int) – The lowest bit included in the slice (inclusive) zero indexed, so the first bit is 0
high (int) – The highest bit included in the slice (inclusive)

Returns:

A new pdarray containing the bits of self from low to high

Return type:

Raises:

RuntimeError – Raised if there is a server-side error thrown

Examples

>>> p = ak.array([2**65 + (2**64 - 1)])
>>> bin(p[0])
'0b101111111111111111111111111111111111111111111111111111111111111111'
>>> bin(p.slice_bits(64, 65)[0])
'0b10'
>>> a = ak.array([143,15])
>>> a.slice_bits(1,3)
array([7 7])
>>> a.slice_bits(4,9)
array([8 0])
>>> a.slice_bits(1,9)
array([71 7])

std(ddof: arkouda.numpy.dtypes.int_scalars = 0) → numpy.float64[source]¶: Compute the standard deviation. See arkouda.std for details.

sum(axis: int | Tuple[int, Ellipsis] | None = None, keepdims: bool = False) → arkouda.numpy.dtypes.numpy_scalars | pdarray[source]¶

Return sum of array elements along the given axis.

Parameters:

axis (int, Tuple[int, ...], optional, default = None) – The axis or axes along which to do the operation If None, the computation is done across the entire array.
keepdims (bool, optional, default = False) – Whether to keep the singleton dimension(s) along axis in the result.

Returns:

numpy_scalar if axis is omitted, in which case operation is done over entire array pdarray if axis is supplied, in which case the operation is done along that axis

Return type:

numpy_scalar or pdarray

Raises:

TypeError – Raised if pda is not a pdarray instance
RuntimeError – Raised if there’s a server-side error thrown

Examples

>>> ak.sum(ak.array([1,2,3,4,5]))
15
>>> ak.sum(ak.array([5.5,4.5,3.5,2.5,1.5]))
17.5
>>> ak.array([[1,2,3],[5,4,3]]).sum(axis=1)
array([6 12])

Notes

Works as a method of a pdarray (e.g. a.sum()) or a standalone function (e.g. ak.sum(a))

to_csv(prefix_path: str, dataset: str = 'array', col_delim: str = ',', overwrite: bool = False)[source]¶

Write pdarry to CSV file(s). File will contain a single column with the pdarray data. All CSV files written by Arkouda include a header denoting data types of the columns.

Parameters:

prefix_path (str) – filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
dataset (str, defaults to "array") – column name to save the pdarray under.
col_delim (str, defaults to ",") – value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
overwrite (bool, defaults to False) – If True, existing files matching the provided path will be overwritten. if False and existing files are found, an error will be returned.

Returns:

response message

Return type:

str

Raises:

ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist
RuntimeError – Raised if one or more of the specified files cannot be opened. if ‘allow_errors’ is true, this may be raised if no values are returned from the server.
TypeError – Raise if the server returns an unknown arkouda_type

Notes

CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for all column names and data
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (”n”) at this time.

to_cuda()[source]¶

Convert the array to a Numba DeviceND array, transferring array data from the arkouda server to Python via ndarray. If the array exceeds a builtin size limit, a RuntimeError is raised.

Returns:

A Numba ndarray with the same attributes and data as the pdarray; on GPU

Return type:

numba.DeviceNDArray

Raises:

ImportError – Raised if CUDA is not available
ModuleNotFoundError – Raised if Numba is either not installed or not enabled
RuntimeError – Raised if there is a server-side error thrown in the course of retrieving the pdarray.

Notes

See also

array

Examples

>>> a = ak.arange(0, 5, 1)
>>> a.to_cuda()
array([0, 1, 2, 3, 4])
>>> type(a.to_cuda())
numpy.devicendarray

to_hdf(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', file_type: str = 'distribute') → str[source]¶

Save the pdarray to HDF5. The object can be saved to a collection of files or single file.

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files (must not already exist)
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, attempt to create new dataset in existing files.
file_type (str ("single" | "distribute")) – Default: “distribute” When set to single, dataset is written to a single file. When distribute, dataset is written on a file per locale. This is only supported by HDF5 files and will have no impact of Parquet Files.

Return type:

string message indicating result of save operation

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

The prefix_path must be visible to the arkouda server and the user must

Examples

>>> a = ak.arange(25)
>>> # Saving without an extension
>>> a.to_hdf('path/prefix', dataset='array')
Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####``
>>> # Saving with an extension (HDF5)
>>> a.to_hdf('path/prefix.h5', dataset='array')
Saves the array to numLocales HDF5 files with the name
``cwd/path/name_prefix_LOCALE####.h5`` where #### is replaced by each locale number
>>> # Saving to a single file
>>> a.to_hdf('path/prefix.hdf5', dataset='array', file_type='single')
Saves the array in to single hdf5 file on the root node.
``cwd/path/name_prefix.hdf5``

to_list() → List[source]¶

Convert the array to a list, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.

Returns:: A list with the same data as the pdarray
Return type:: list
Raises:: RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes

Notes

See also

Examples

>>> a = ak.arange(0, 5, 1)
>>> a.to_list()
[0, 1, 2, 3, 4]
>>> type(a.to_list())
<class 'list'>

to_ndarray() → numpy.ndarray[source]¶

Convert the array to a np.ndarray, transferring array data from the Arkouda server to client-side Python. Note: if the pdarray size exceeds client.maxTransferBytes, a RuntimeError is raised.

Returns:: A numpy ndarray with the same attributes and data as the pdarray
Return type:: np.ndarray
Raises:: RuntimeError – Raised if there is a server-side error thrown, if the pdarray size exceeds the built-in client.maxTransferBytes size limit, or if the bytes received does not match expected number of bytes

Notes

See also

Examples

>>> a = ak.arange(0, 5, 1)
>>> a.to_ndarray()
array([0, 1, 2, 3, 4])
>>> type(a.to_ndarray())
<class 'numpy.ndarray'>

to_parquet(prefix_path: str, dataset: str = 'array', mode: str = 'truncate', compression: str | None = None) → str[source]¶

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files (must not already exist)
mode (str {'truncate' | 'append'}) – By default, truncate (overwrite) output files, if they exist. If ‘append’, attempt to create new dataset in existing files.
compression (str (Optional)) – (None | “snappy” | “gzip” | “brotli” | “zstd” | “lz4”) Sets the compression type used with Parquet files

Return type:

string message indicating result of save operation

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

The prefix_path must be visible to the arkouda server and the user must

Examples

>>> a = ak.arange(25)
>>> # Saving without an extension
>>> a.to_parquet('path/prefix', dataset='array')
Saves the array to numLocales HDF5 files with the name ``cwd/path/name_prefix_LOCALE####``
>>> # Saving with an extension (HDF5)
>>> a.to_parqet('path/prefix.parquet', dataset='array')
Saves the array to numLocales HDF5 files with the name
``cwd/path/name_prefix_LOCALE####.parquet`` where #### is replaced by each locale number

transfer(hostname: str, port: arkouda.numpy.dtypes.int_scalars)[source]¶

Sends a pdarray to a different Arkouda server

Parameters:

hostname (str) – The hostname where the Arkouda server intended to receive the pdarray is running.
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to ak.receive_array().

Return type:

A message indicating a complete transfer

Raises:

ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype

unregister() → None[source]¶

Unregister a pdarray in the arkouda server which was previously registered using register() and/or attahced to using attach()

Return type:: None
Raises:: RuntimeError – Raised if the server could not find the internal name/symbol to remove

Notes

Registered names/pdarrays in the server are immune to deletion until they are unregistered.

Examples

>>> a = zeros(100)
>>> a.register("my_zeros")
>>> # potentially disconnect from server and reconnect to server
>>> b = ak.pdarray.attach("my_zeros")
>>> # ...other work...
>>> b.unregister()

update_hdf(prefix_path: str, dataset: str = 'array', repack: bool = True)[source]¶

Overwrite the dataset with the name provided with this pdarray. If the dataset does not exist it is added

Parameters:

prefix_path (str) – Directory and filename prefix that all output files share
dataset (str) – Name of the dataset to create in files
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.

Return type:

str - success message if successful

Raises:

RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the dataset provided does not exist, it will be added

value_counts()[source]¶

Count the occurrences of the unique values of self.

Returns:

unique_values (pdarray) – The unique values, sorted in ascending order
counts (pdarray, int64) – The number of times the corresponding unique value occurs

Examples

>>> ak.array([2, 0, 2, 4, 0, 0]).value_counts()
(array([0, 2, 4]), array([3, 2, 1]))

var(ddof: arkouda.numpy.dtypes.int_scalars = 0) → numpy.float64[source]¶: Compute the variance. See arkouda.var for details.

arkouda.plot_dist(b, h, log=True, xlabel=None, newfig=True)[source]¶

Plot the distribution and cumulative distribution of histogram Data

Parameters:

b (np.ndarray) – Bin edges
h (np.ndarray) – Histogram data
log (bool) – use log to scale y
xlabel (str) – Label for the x axis of the graph
newfig (bool) – Generate a new figure or not

Notes

This function does not return or display the plot. A user must have matplotlib imported in addition to arkouda to display plots. This could be updated to return the object or have a flag to show the resulting plots. See Examples Below.

Examples

>>> import arkouda as ak
>>> from matplotlib import pyplot as plt
>>> b, h = ak.histogram(ak.arange(10), 3)
>>> ak.plot_dist(b, h.to_ndarray())
>>> # to show the plot
>>> plt.show()

arkouda.popcount(pda: pdarray) → pdarray[source]¶

Find the population (number of bits set) for each integer in an array.

Parameters:: pda (pdarray, int64, uint64, bigint) – Input array (must be integral).
Returns:: population – The number of bits set (1) in each element
Return type:: pdarray
Raises:: TypeError – If input array is not int64, uint64, or bigint

Examples

>>> A = ak.arange(10)
>>> ak.popcount(A)
array([0, 1, 1, 2, 1, 2, 2, 3, 1, 2])

arkouda.power(pda: pdarray, pwr: int | float | pdarray, where: arkouda.numpy.dtypes.bool_scalars | pdarray = True) → pdarray[source]¶

Raises an array to a power. If where is given, the operation will only take place in the positions where the where condition is True.

Note: Our implementation of the where argument deviates from numpy. The difference in behavior occurs at positions where the where argument contains a False. In numpy, these position will have uninitialized memory (which can contain anything and will vary between runs). We have chosen to instead return the value of the original array in these positions.

Parameters:

pda (pdarray) – A pdarray of values that will be raised to a power (pwr)
pwr (integer, float, or pdarray) – The power(s) that pda is raised to
where (Boolean or pdarray) – This condition is broadcast over the input. At locations where the condition is True, the corresponding value will be raised to the respective power. Elsewhere, it will retain its original value. Default set to True.

Returns:

a pdarray of values raised to a power, under the boolean where condition.

Return type:

Examples

>>> a = ak.arange(5)
>>> ak.power(a, 3)
array([0, 1, 8, 27, 64])
>>> ak.power(a), 3, a % 2 == 0)
array([0, 1, 8, 3, 64])

Raises:

TypeError – raised if pda is not a pdarray, or if pwe is not an int, float, or pdarray
ValueError – raised if pda and power are of incompatible dimensions

arkouda.pretty_print_information(names: List[str] | str = RegisteredSymbols) → None[source]¶

Prints verbose information for each object in names in a human readable format

Parameters:: names (Union[List[str], str]) – names is either the name of an object or list of names of objects to retrieve info if names is ak.AllSymbols, retrieves info for all symbols in the symbol table if names is ak.RegisteredSymbols, retrieves info for all symbols in the registry
Return type:: None
Raises:: RuntimeError – Raised if a server-side error is thrown in the process of retrieving information about the objects in names

arkouda.promote_to_common_dtype(arrays: List[arkouda.numpy.pdarrayclass.pdarray]) → Tuple[Any, List[arkouda.numpy.pdarrayclass.pdarray]][source]¶

Promote a list of pdarrays to a common dtype.

Parameters:: arrays (List[pdarray]) – List of pdarrays to promote
Returns:: The common dtype of the pdarrays and the list of pdarrays promoted to that dtype
Return type:: dtype, List[pdarray]
Raises:: TypeError – Raised if any pdarray is a non-numeric type

See also

pdarray.promote_dtype

Examples

>>> a = ak.arange(5)
>>> b = ak.ones(5, dtype=ak.float64)
>>> dtype, promoted = ak.promote_to_common_dtype([a, b])
>>> dtype
dtype('float64')
>>> all(isinstance(p, ak.pdarray) and p.dtype == dtype for p in promoted)
True

arkouda.randint(low: arkouda.numpy.dtypes.numeric_scalars, high: arkouda.numpy.dtypes.numeric_scalars, size: arkouda.numpy.dtypes.int_scalars | Tuple[arkouda.numpy.dtypes.int_scalars, Ellipsis] = 1, dtype=akint64, seed: arkouda.numpy.dtypes.int_scalars | None = None) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Generate a pdarray of randomized int, float, or bool values in a specified range bounded by the low and high parameters.

Parameters:

low (numeric_scalars) – The low value (inclusive) of the range
high (numeric_scalars) – The high value (exclusive for int, inclusive for float) of the range
size (int_scalars or tuple of int_scalars) – The size or shape of the returned array
dtype (Union[int64, float64, bool]) – The dtype of the array
seed (int_scalars, optional) – Index for where to pull the first returned value

Returns:

Values drawn uniformly from the specified range having the desired dtype

Return type:

Raises:

TypeError – Raised if dtype.name not in DTypes, size is not an int, low or high is not an int or float, or seed is not an int
ValueError – Raised if size < 0 or if high < low

Notes

Calling randint with dtype=float64 will result in uniform non-integral floating point values.

Ranges >= 2**64 in size is undefined behavior because it exceeds the maximum value that can be stored on the server (uint64)

Examples

>>> ak.randint(0, 10, 5, seed=1701)
array([6 5 1 6 3])

>>> ak.randint(0, 1, 3, seed=1701, dtype=ak.float64)
array([0.011410423448327005 0.73618171558685619 0.12367222192448891])

>>> ak.randint(0, 1, 5, seed=1701, dtype=ak.bool_)
array([False True False True False])

arkouda.random_strings_lognormal(logmean: arkouda.numpy.dtypes.numeric_scalars, logstd: arkouda.numpy.dtypes.numeric_scalars, size: arkouda.numpy.dtypes.int_scalars, characters: str = 'uppercase', seed: arkouda.numpy.dtypes.int_scalars | None = None) → arkouda.numpy.strings.Strings[source]¶

Generate random strings with log-normally distributed lengths and with characters drawn from a specified set.

Parameters:

logmean (numeric_scalars) – The log-mean of the length distribution
logstd (numeric_scalars) – The log-standard-deviation of the length distribution
size (int_scalars) – The number of strings to generate
characters ((uppercase, lowercase, numeric, printable, binary)) – The set of characters to draw from
seed (int_scalars, optional) – Value used to initialize the random number generator

Returns:

The Strings object encapsulating a pdarray of random strings

Return type:

random_strings_lognormal, randint

Raises:

TypeError – Raised if logmean is neither a float nor a int, logstd is not a float, seed is not an int, size is not an int, or if characters is not a str
ValueError – Raised if logstd <= 0 or size < 0

See also

Notes

The lengths of the generated strings are distributed $Lognormal(mu, sigma^2)$, with $\mu = logmean$ and $\sigma = logstd$. Thus, the strings will have an average length of $exp(\mu + 0.5*\sigma^2)$, a minimum length of zero, and a heavy tail towards longer strings.

Examples

>>> ak.random_strings_lognormal(2, 0.25, 5, seed=1)
array(['VWHJEX', 'BEBBXJHGM', 'RWOVKBUR', 'LNJCSDXD', 'NKEDQC'])

>>> ak.random_strings_lognormal(2, 0.25, 5, seed=1, characters='printable')
array(['eL96<O', ')o-GOe lR', ')PV yHf(', '._b3Yc&K', ',7Wjef'])

arkouda.random_strings_uniform(minlen: arkouda.numpy.dtypes.int_scalars, maxlen: arkouda.numpy.dtypes.int_scalars, size: arkouda.numpy.dtypes.int_scalars, characters: str = 'uppercase', seed: None | arkouda.numpy.dtypes.int_scalars = None) → arkouda.numpy.strings.Strings[source]¶

Generate random strings with lengths uniformly distributed between minlen and maxlen, and with characters drawn from a specified set.

Parameters:

minlen (int_scalars) – The minimum allowed length of string
maxlen (int_scalars) – The maximum allowed length of string
size (int_scalars) – The number of strings to generate
characters ((uppercase, lowercase, numeric, printable, binary)) – The set of characters to draw from
seed (Union[None, int_scalars], optional) – Value used to initialize the random number generator

Returns:

The array of random strings

Return type:

random_strings_lognormal, randint

Raises:

ValueError – Raised if minlen < 0, maxlen < minlen, or size < 0

See also

Examples

>>> ak.random_strings_uniform(minlen=1, maxlen=5, seed=8675309, size=5)
array(['ECWO', 'WSS', 'TZG', 'RW', 'C'])

>>> ak.random_strings_uniform(minlen=1, maxlen=5, seed=8675309, size=5,
... characters='printable')
array(['2 .z', 'aom', '2d|', 'o(', 'M'])

arkouda.read(filenames: str | List[str], datasets: str | List[str] | None = None, iterative: bool = False, strictTypes: bool = True, allow_errors: bool = False, calc_string_offsets=False, column_delim: str = ',', read_nested: bool = True, has_non_float_nulls: bool = False, fixed_len: int = -1) → Mapping[str, arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.numpy.timeclass.Datetime | arkouda.numpy.timeclass.Timedelta | arkouda.index.Index][source]¶

Read datasets from files. File Type is determined automatically.

Parameters:

filenames (list or str) – Either a list of filenames or shell expression
datasets (list or str or None) – (List of) name(s) of dataset(s) to read (default: all available)
iterative (bool) – Iterative (True) or Single (False) function call(s) to server
strictTypes (bool) – If True (default), require all dtypes of a given dataset to have the same precision and sign. If False, allow dtypes of different precision and sign across different files. For example, if one file contains a uint32 dataset and another contains an int64 dataset with the same name, the contents of both will be read into an int64 pdarray.
allow_errors (bool) – Default False, if True will allow files with read errors to be skipped instead of failing. A warning will be included in the return containing the total number of files skipped due to failure and up to 10 filenames.
calc_string_offsets (bool) – Default False, if True this will tell the server to calculate the offsets/segments array on the server versus loading them from HDF5 files. In the future this option may be set to True as the default.
column_delim (str) – Column delimiter to be used if dataset is CSV. Otherwise, unused.
read_nested (bool) – Default True, when True, SegArray objects will be read from the file. When False, SegArray (or other nested Parquet columns) will be ignored. Ignored if datasets is not None Parquet Files only.
has_non_float_nulls (bool) – Default False. This flag must be set to True to read non-float parquet columns that contain null values.
fixed_len (int) – Default -1. This value can be set for reading Parquet string columns when the length of each string is known at runtime. This can allow for skipping byte calculation, which can have an impact on performance.

Returns:

Dictionary of {datasetName: pdarray, String, or SegArray}

Return type:

Returns a dictionary of Arkouda pdarrays, Arkouda Strings, or Arkouda Segarrays.

Raises:

RuntimeError – If invalid filetype is detected

Notes

If filenames is a string, it is interpreted as a shell expression (a single filename is a valid expression, so it will work) and is expanded with glob to read all matching files.

If iterative == True each dataset name and file names are passed to the server as independent sequential strings while if iterative == False all dataset names and file names are passed to the server in a single string.

If datasets is None, infer the names of datasets from the first file and read all of them. Use get_datasets to show the names of datasets to HDF5/Parquet files.

CSV files without the Arkouda Header are not supported.

Examples

Read with file Extension >>> x = ak.read(‘path/name_prefix.h5’) # load HDF5 - processing determines file type not extension Read without file Extension >>> x = ak.read(‘path/name_prefix.parquet’) # load Parquet Read Glob Expression >>> x = ak.read(‘path/name_prefix*’) # Reads HDF5

Read CSV file(s) into Arkouda objects. If more than one dataset is found, the objects will be returned in a dictionary mapping the dataset name to the Arkouda object containing the data. If the file contains the appropriately formatted header, typed data will be returned. Otherwise, all data will be returned as a Strings object.

Parameters:

filenames (str or List[str]) – The filenames to read data from
datasets (str or List[str] (Optional)) – names of the datasets to read. When None, all datasets will be read.
column_delim (str) – The delimiter for column names and data. Defaults to “,”.
allow_errors (bool) – Default False, if True will allow files with read errors to be skipped instead of failing. A warning will be included in the return containing the total number of files skipped due to failure and up to 10 filenames.

Returns:

Dictionary of {datasetName: pdarray, String, or SegArray}

Return type:

Returns a dictionary of Arkouda pdarrays, Arkouda Strings, or Arkouda Segarrays.

Raises:

ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server

See also

to_csv

Notes

CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for column names and data
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (\n) at this time.
Unlike other file formats, CSV files store Strings as their UTF-8 format instead of storing bytes as uint(8).

arkouda.read_hdf(filenames: str | List[str], datasets: str | List[str] | None = None, iterative: bool = False, strict_types: bool = True, allow_errors: bool = False, calc_string_offsets: bool = False, tag_data=False) → Mapping[str, arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.numpy.timeclass.Datetime | arkouda.numpy.timeclass.Timedelta | arkouda.index.Index][source]¶

Read Arkouda objects from HDF5 file/s

Parameters:

filenames (str, List[str]) – Filename/s to read objects from
datasets (Optional str, List[str]) – datasets to read from the provided files
iterative (bool) – Iterative (True) or Single (False) function call(s) to server
strict_types (bool) – If True (default), require all dtypes of a given dataset to have the same precision and sign. If False, allow dtypes of different precision and sign across different files. For example, if one file contains a uint32 dataset and another contains an int64 dataset with the same name, the contents of both will be read into an int64 pdarray.
allow_errors (bool) – Default False, if True will allow files with read errors to be skipped instead of failing. A warning will be included in the return containing the total number of files skipped due to failure and up to 10 filenames.
calc_string_offsets (bool) – Default False, if True this will tell the server to calculate the offsets/segments array on the server versus loading them from HDF5 files. In the future this option may be set to True as the default.
tagData (bool) – Default False, if True tag the data with the code associated with the filename that the data was pulled from.

Returns:

Dictionary of {datasetName: pdarray, String, SegArray}

Return type:

Returns a dictionary of Arkouda pdarrays, Arkouda Strings, or Arkouda Segarrays.

Raises:

ValueError – Raised if all datasets are not present in all hdf5 files or if one or more of the specified files do not exist
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server

Notes

If filenames is a string, it is interpreted as a shell expression (a single filename is a valid expression, so it will work) and is expanded with glob to read all matching files.

If datasets is None, infer the names of datasets from the first file and read all of them. Use get_datasets to show the names of datasets to HDF5 files.

See also

read_tagged_data

Examples

>>>
# Read with file Extension
>>> x = ak.read_hdf('path/name_prefix.h5') # load HDF5
# Read Glob Expression
>>> x = ak.read_hdf('path/name_prefix*') # Reads HDF5

arkouda.read_parquet(filenames: str | List[str], datasets: str | List[str] | None = None, iterative: bool = False, strict_types: bool = True, allow_errors: bool = False, tag_data: bool = False, read_nested: bool = True, has_non_float_nulls: bool = False, fixed_len: int = -1) → Mapping[str, arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray | arkouda.categorical.Categorical | arkouda.dataframe.DataFrame | arkouda.client_dtypes.IPv4 | arkouda.numpy.timeclass.Datetime | arkouda.numpy.timeclass.Timedelta | arkouda.index.Index][source]¶

Read Arkouda objects from Parquet file/s

Parameters:

filenames (str, List[str]) – Filename/s to read objects from
datasets (Optional str, List[str]) – datasets to read from the provided files
iterative (bool) – Iterative (True) or Single (False) function call(s) to server
strict_types (bool) – If True (default), require all dtypes of a given dataset to have the same precision and sign. If False, allow dtypes of different precision and sign across different files. For example, if one file contains a uint32 dataset and another contains an int64 dataset with the same name, the contents of both will be read into an int64 pdarray.
allow_errors (bool) – Default False, if True will allow files with read errors to be skipped instead of failing. A warning will be included in the return containing the total number of files skipped due to failure and up to 10 filenames.
tagData (bool) – Default False, if True tag the data with the code associated with the filename that the data was pulled from.
read_nested (bool) – Default True, when True, SegArray objects will be read from the file. When False, SegArray (or other nested Parquet columns) will be ignored. If datasets is not None, this will be ignored.
has_non_float_nulls (bool) – Default False. This flag must be set to True to read non-float parquet columns that contain null values.
fixed_len (int) – Default -1. This value can be set for reading Parquet string columns when the length of each string is known at runtime. This can allow for skipping byte calculation, which can have an impact on performance.

Returns:

Dictionary of {datasetName: pdarray, String, or SegArray}

Return type:

Returns a dictionary of Arkouda pdarrays, Arkouda Strings, or Arkouda Segarrays.

Raises:

ValueError – Raised if all datasets are not present in all parquet files or if one or more of the specified files do not exist
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server

Notes

If filenames is a string, it is interpreted as a shell expression (a single filename is a valid expression, so it will work) and is expanded with glob to read all matching files.

If datasets is None, infer the names of datasets from the first file and read all of them. Use get_datasets to show the names of datasets to Parquet files.

Parquet always recomputes offsets at this time This will need to be updated once parquets workflow is updated

See also

read_tagged_data

Examples

Read without file Extension >>> x = ak.read_parquet(‘path/name_prefix.parquet’) # load Parquet Read Glob Expression >>> x = ak.read_parquet(‘path/name_prefix*’) # Reads Parquet

arkouda.read_tagged_data(filenames: str | List[str], datasets: str | List[str] | None = None, strictTypes: bool = True, allow_errors: bool = False, calc_string_offsets=False, read_nested: bool = True, has_non_float_nulls: bool = False)[source]¶

Read datasets from files and tag each record to the file it was read from. File Type is determined automatically.

Parameters:

filenames (list or str) – Either a list of filenames or shell expression
datasets (list or str or None) – (List of) name(s) of dataset(s) to read (default: all available)
strictTypes (bool) – If True (default), require all dtypes of a given dataset to have the same precision and sign. If False, allow dtypes of different precision and sign across different files. For example, if one file contains a uint32 dataset and another contains an int64 dataset with the same name, the contents of both will be read into an int64 pdarray.
allow_errors (bool) – Default False, if True will allow files with read errors to be skipped instead of failing. A warning will be included in the return containing the total number of files skipped due to failure and up to 10 filenames.
calc_string_offsets (bool) – Default False, if True this will tell the server to calculate the offsets/segments array on the server versus loading them from HDF5 files. In the future this option may be set to True as the default.
read_nested (bool) – Default True, when True, SegArray objects will be read from the file. When False, SegArray (or other nested Parquet columns) will be ignored. Ignored if datasets is not None Parquet Files only.
has_non_float_nulls (bool) – Default False. This flag must be set to True to read non-float parquet columns that contain null values.

Notes

Not currently supported for Categorical or GroupBy datasets

Examples

Read files and return data with tagging corresponding to the Categorical returned cat.codes will link the codes in data to the filename. Data will contain the code Filename_Codes >>> data, cat = ak.read_tagged_data(‘path/name’) >>> data {‘Filname_Codes’: array([0 3 6 9 12]), ‘col_name’: array([0 0 0 1])}

arkouda.read_zarr(store_path: str, ndim: int, dtype)[source]¶

Reads a Zarr store from disk into a pdarray. Supports multi-dimensional pdarrays of numeric types. To use this function, ensure you have installed the blosc dependency (make install-blosc) and have included ZarrMsg.chpl in the ServerModules.cfg file.

Parameters:

store_path (str) – The path to the Zarr store. The path must be to a directory that contains a .zarray file containing the Zarr store metadata.
ndim (int) – The number of dimensions in the array
dtype (str) – The data type of the array

Returns:

The pdarray read from the Zarr store.

Return type:

arkouda.receive(hostname: str, port)[source]¶

Receive a pdarray sent by pdarray.transfer().

Parameters:

hostname (str) – The hostname of the pdarray that sent the array
port (int_scalars) – The port to send the array over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to pdarray.transfer().

Returns:

The pdarray sent from the sending server to the current receiving server.

Return type:

Raises:

ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype

arkouda.receive_dataframe(hostname: str, port)[source]¶

Receive a pdarray sent by dataframe.transfer().

Parameters:

hostname (str) – The hostname of the dataframe that sent the array
port (int_scalars) – The port to send the dataframe over. This needs to be an open port (i.e., not one that the Arkouda server is running on). This will open up numLocales ports, each of which in succession, so will use ports of the range {port..(port+numLocales)} (e.g., running an Arkouda server of 4 nodes, port 1234 is passed as port, Arkouda will use ports 1234, 1235, 1236, and 1237 to send the array data). This port much match the port passed to the call to pdarray.send_array().

Returns:

The dataframe sent from the sending server to the current receiving server.

Return type:

Raises:

ValueError – Raised if the op is not within the pdarray.BinOps set
TypeError – Raised if other is not a pdarray or the pdarray.dtype is not a supported dtype

arkouda.register_all(data: dict)[source]¶

Parameters:: data (dict) – Maps name to register the object to the object. For example, {“MyArray”: ak.array([0, 1, 2])
Return type:: None

Repeat each element of an array after themselves

Parameters:

a (int, Sequence of int, or pdarray) – Input array.
repeats (int, Sequence of int, or pdarray) – The number of repetitions for each element. repeats is broadcasted to fit the shape of the given axis.
axis (int, optional) – The axis along which to repeat values. By default, use the flattened input array, and return a flat output array.

Returns:

Output array which has the same shape as a, except along the given axis.

Return type:

Examples

>>> ak.repeat(3, 4)
array([3 3 3 3])
>>> x = ak.array([[1,2],[3,4]])
>>> ak.repeat(x, 2)
array([1 1 2 2 3 3 4 4])
>>> ak.repeat(x, 3, axis=1)
array([array([1 1 1 2 2 2]) array([3 3 3 4 4 4])])
>>> ak.repeat(x, [1, 2], axis=0)
array([array([1 2]) array([3 4]) array([3 4])])

arkouda.resolve_scalar_dtype(val: object) → str[source]¶

Try to infer what dtype arkouda_server should treat val as.

Parameters:: val (object) – The object to determine the dtype of.
Returns:: The dtype name, if it can be resolved, otherwise the type (as str).
Return type:: str

Examples

>>> ak.resolve_scalar_dtype(1)
'int64'
>>> ak.resolve_scalar_dtype(2.0)
'float64'

arkouda.restore(filename)[source]¶

Return data saved using ak.snapshot

Parameters:

filename (str)
read (Name used to create snapshot to be)

Return type:

Dict

Notes

Unlike other save/load methods using snapshot restore will save DataFrames alongside other objects in HDF5. Thus, they are returned within the dictionary as a dataframe.

arkouda.rotl(x, rot) → pdarray[source]¶

Rotate bits of <x> to the left by <rot>.

Parameters:

x (pdarray(int64/uint64) or integer) – Value(s) to rotate left.
rot (pdarray(int64/uint64) or integer) – Amount(s) to rotate by.

Returns:

rotated – The rotated elements of x.

Return type:

pdarray(int64/uint64)

Raises:

TypeError – If input array is not int64 or uint64

Examples

>>> A = ak.arange(10)
>>> ak.rotl(A, A)
array([0, 2, 8, 24, 64, 160, 384, 896, 2048, 4608])

arkouda.rotr(x, rot) → pdarray[source]¶

Rotate bits of <x> to the left by <rot>.

Parameters:

x (pdarray(int64/uint64) or integer) – Value(s) to rotate left.
rot (pdarray(int64/uint64) or integer) – Amount(s) to rotate by.

Returns:

rotated – The rotated elements of x.

Return type:

pdarray(int64/uint64)

Raises:

TypeError – If input array is not int64 or uint64

Examples

>>> A = ak.arange(10)
>>> ak.rotr(1024 * A, A)
array([0, 512, 512, 384, 256, 160, 96, 56, 32, 18])

arkouda.save_all(columns: Mapping[str, arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray] | List[arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray], prefix_path: str, names: List[str] | None = None, file_format='HDF5', mode: str = 'truncate', file_type: str = 'distribute', compression: str | None = None) → None[source]¶

DEPRECATED Save multiple named pdarrays to HDF5/Parquet files. :param columns: Collection of arrays to save :type columns: dict or list of pdarrays :param prefix_path: Directory and filename prefix for output files :type prefix_path: str :param names: Dataset names for the pdarrays :type names: list of str :param file_format: ‘HDF5’ or ‘Parquet’. Defaults to hdf5 :type file_format: str :param mode: By default, truncate (overwrite) the output files if they exist.

If ‘append’, attempt to create new dataset in existing files.

Parameters:

file_type (str ("single" | "distribute")) – Default: distribute Single writes the dataset to a single file Distribute writes the dataset to a file per locale Only used with HDF5
compression (str (None | "snappy" | "gzip" | "brotli" | "zstd" | "lz4")) – Optional Select the compression to use with Parquet files. Only used with Parquet.

Return type:

None

Raises:

ValueError – Raised if (1) the lengths of columns and values differ or (2) the mode is not ‘truncate’ or ‘append’

Notes

Creates one file per locale containing that locale’s chunk of each pdarray. If columns is a dictionary, the keys are used as the HDF5 dataset names. Otherwise, if no names are supplied, 0-up integers are used. By default, any existing files at path_prefix will be overwritten, unless the user specifies the ‘append’ mode, in which case arkouda will attempt to add <columns> as new datasets to existing files. If the wrong number of files is present or dataset names already exist, a RuntimeError is raised.

Examples

>>> a = ak.arange(25)
>>> b = ak.arange(25)
>>> # Save with mapping defining dataset names
>>> ak.save_all({'a': a, 'b': b}, 'path/name_prefix', file_format='Parquet')
>>> # Save using names instead of mapping
>>> ak.save_all([a, b], 'path/name_prefix', names=['a', 'b'], file_format='Parquet')

arkouda.save_checkpoint(name='', path='.akdata', mode: str = 'overwrite')[source]¶

Save the server’s state. Records some metadata about the server, and saves all pdarrays into parquet files.

Parameters:

name (str) – Name of the checkpoint. The default will be the server session ID, which is typically in format id_<hash>_. A directory will be created in path with this name.
path (str) – The directory to save the checkpoint. If the directory doesn’t exist, it will be created. If it exists, a new directory for the checkpoint instance will be created inside this directory.
mode ({'overwrite' | 'error'}) – By default, overwrite the checkpoint files if they exist. If ‘error’, an error will be raised if a checkpoint with the same name exists.

Notes

Only ``pdarray``s are saved. Other data structures will not be recorded. We expect to expand the coverage in the future.

Returns:: The checkpoint name, which will be the same as the name argument if it was passed.
Return type:: str

Examples

>>> arr = ak.zeros(10, int)
>>> arr[2] = 2
>>> arr[2]
2
>>> cp_name = ak.save_checkpoint()
>>> arr[2] = 3
>>> arr[2]
3
>>> ak.load_checkpoint(cp_name)
>>> arr[2]
2

See also

load_checkpoint

arkouda.scalar_array(value: arkouda.numpy.dtypes.numeric_scalars, dtype: numpy.dtype | type | str | arkouda.numpy.dtypes.bigint | None = None) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Create a pdarray from a single scalar value.

Parameters:: value (numeric_scalars) – Value to create pdarray from
Returns:: pdarray with a single element
Return type:: pdarray

Examples

>>> ak.scalar_array(5)
array([5])

>>> ak.scalar_array(7.0)
array([7.00000000000000000])

Raises:: RuntimeError – Raised if value cannot be cast as dtype

arkouda.segarray(segments: arkouda.numpy.pdarrayclass.pdarray, values: arkouda.numpy.pdarrayclass.pdarray, lengths=None, grouping=None)[source]¶: Alias for the from_parts function. Prevents user from needing to call ak.SegArray constructor DEPRECATED

arkouda.setdiff1d(A: arkouda.groupbyclass.groupable, B: arkouda.groupbyclass.groupable, assume_unique: bool = False) → arkouda.numpy.pdarrayclass.pdarray | arkouda.groupbyclass.groupable[source]¶

Find the set difference of two arrays.

Return the sorted, unique values in A that are not in B.

Parameters:

A (list of pdarrays, pdarray, Strings, or Categorical)
B (list of pdarrays, pdarray, Strings, or Categorical)
assume_unique (bool) – If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False.

Returns:

Sorted 1D array/List of sorted pdarrays of values in A that are not in B.

Return type:

pdarray/groupable

Raises:

TypeError – Raised if either A or B is not a pdarray
RuntimeError – Raised if the dtype of either pdarray is not supported

Notes

ak.setdiff1d is not supported for bool pdarrays

Examples

>>> a = ak.array([1, 2, 3, 2, 4, 1])
>>> b = ak.array([3, 4, 5, 6])
>>> ak.setdiff1d(a, b)
array([1 2])

Multi-Array Example

>>> a = ak.arange(1, 6)
>>> b = ak.array([1, 5, 3, 4, 2])
>>> c = ak.array([1, 4, 3, 2, 5])
>>> d = ak.array([1, 2, 3, 5, 4])
>>> multia = [a, a, a]
>>> multib = [b, c, d]
>>> ak.setdiff1d(multia, multib)
[array([2 4 5]), array([2 4 5]), array([2 4 5])]

arkouda.setxor1d(A: arkouda.groupbyclass.groupable, B: arkouda.groupbyclass.groupable, assume_unique: bool = False) → arkouda.numpy.pdarrayclass.pdarray | arkouda.groupbyclass.groupable[source]¶

Find the set exclusive-or (symmetric difference) of two arrays.

Return the sorted, unique values that are in only one (not both) of the input arrays.

Parameters:

A (list of pdarrays, pdarray, Strings, or Categorical)
B (list of pdarrays, pdarray, Strings, or Categorical)
assume_unique (bool) – If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False.

Returns:

Sorted 1D array/List of sorted pdarrays of unique values that are in only one of the input arrays.

Return type:

pdarray/groupable

Raises:

TypeError – Raised if either A or B is not a groupable
RuntimeError – Raised if the dtype of either pdarray is not supported

Examples

>>> a = ak.array([1, 2, 3, 2, 4])
>>> b = ak.array([2, 3, 5, 7, 5])
>>> ak.setxor1d(a,b)
array([1 4 5 7])

Multi-Array Example

>>> a = ak.arange(1, 6)
>>> b = ak.array([1, 5, 3, 4, 2])
>>> c = ak.array([1, 4, 3, 2, 5])
>>> d = ak.array([1, 2, 3, 5, 4])
>>> multia = [a, a, a]
>>> multib = [b, c, d]
>>> ak.setxor1d(multia, multib)
[array([2 2 4 4 5 5]), array([2 5 2 4 4 5]), array([2 4 5 4 2 5])]

Return the shape of an array.

Parameters:: a (pdarray) – Input array.
Returns:: shape – The elements of the shape tuple give the lengths of the corresponding array dimensions.
Return type:: tuple of ints

Examples

>>> import arkouda as ak
>>> ak.shape(ak.eye(3,2))
(3, 2)
>>> ak.shape([[1, 3]])
(1, 2)
>>> ak.shape([0])
(1,)
>>> ak.shape(0)
()

arkouda.skew(pda: pdarray, bias: bool = True) → numpy.float64[source]¶

Computes the sample skewness of an array. Skewness > 0 means there’s greater weight in the right tail of the distribution. Skewness < 0 means there’s greater weight in the left tail of the distribution. Skewness == 0 means the data is normally distributed. Based on the scipy.stats.skew function.

Parameters:

pda (pdarray) – A pdarray of ak.int64 or ak.float64 values that will be calculated to find the skew
bias (bool, optional, default = True) – If False, then the calculations are corrected for statistical bias.

Returns:

The skew of all elements in the array

Return type:

np.float64

Examples

>>> a = ak.array([1, 1, 1, 5, 10])
>>> ak.skew(a)
0.9442193396379165
>>> ak.skew(ak.array([9,9,9,5,0]))
-0.9442193396379165
>>> ak.skew(ak.array([10,10,10,10,10]))
0

Raises:

RuntimeError – raised if pda.dtype is ak.bigint
TypeError – raised if pda.dtype is Strings

arkouda.snapshot(filename)[source]¶

Create a snapshot of the current Arkouda namespace. All currently accessible variables containing Arkouda objects will be written to an HDF5 file.

Unlike other save/load functions, this maintains the integrity of dataframes.

Current Variable names are used as the dataset name when saving.

Parameters:

filename (str)
file (Name to use when storing)

Return type:

None

See also

ak.restore

arkouda.sort(pda: arkouda.numpy.pdarrayclass.pdarray, algorithm: SortingAlgorithm = SortingAlgorithm.RadixSortLSD, axis: arkouda.numpy.dtypes.int_scalars = -1) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Return a sorted copy of the array. Only sorts numeric arrays; for Strings, use argsort.

Parameters:

pda (pdarray) – The array to sort (int64, uint64, or float64)
algorithm (SortingAlgorithm, default=SortingAlgorithm.RadixSortLSD) – The algorithm to be used for sorting the arrays.
axis (int_scalars, default=-1) – The axis to sort over. Setting to -1 means that it will sort over axis = ndim - 1.

Returns:

The sorted copy of pda

Return type:

pdarray of int64, uint64, or float64

Raises:

TypeError – Raised if the parameter is not a pdarray
ValueError – Raised if sort attempted on a pdarray with an unsupported dtype such as bool

See also

argsort

Notes

Uses a least-significant-digit radix sort, which is stable and resilient to non-uniformity in data but communication intensive.

Examples

>>> a = ak.randint(0, 10, 10)
>>> sorted = ak.sort(a)
>>> sorted
array([0 1 1 3 4 5 7 8 8 9])

arkouda.sqrt(pda: pdarray, where: arkouda.numpy.dtypes.bool_scalars | pdarray = True) → pdarray[source]¶

Takes the square root of array. If where is given, the operation will only take place in the positions where the where condition is True.

Parameters:

pda (pdarray) – A pdarray of values the square roots of which will be computed
where (Boolean or pdarray) – This condition is broadcast over the input. At locations where the condition is True, the corresponding value will be square rooted. Elsewhere, it will retain its original value. Default set to True.

Returns:

a pdarray of square roots of the original values, or the original values themselves, subject to the boolean where condition.

Return type:

Examples

>>> a = ak.arange(5)
>>> ak.sqrt(a)
array([0.00000000000000000 1.00000000000000000 1.4142135623730951
         1.7320508075688772 2.00000000000000000])
>>> ak.sqrt(a, ak.array([True, True, False, False, True]))
array([0.00000000000000000 1.00000000000000000 2.00000000000000000
         3.00000000000000000 2.00000000000000000])

Raises:: TypeError – raised if pda is not a pdarray of ak.int64 or ak.float64

Notes

Square roots of negative numbers are returned as nan.

Remove degenerate (size one) dimensions from an array.

Parameters:

x (pdarray) – The array to squeeze
axis (int or Tuple[int, ...]) – The axis or axes to squeeze (must have a size of one). If axis = None, all dimensions of size 1 will be squeezed.

Returns:

A copy of x with the dimensions specified in the axis argument removed.

Return type:

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> x = ak.arange(10).reshape((1, 10, 1))
>>> x
array([array([array([0]) array([1]) array([2]) array([3])....
 array([4]) array([5]) array([6]) array([7]) array([8]) array([9])])])
>>> x.shape
(1, 10, 1)
>>> ak.squeeze(x,axis=None)
array([0 1 2 3 4 5 6 7 8 9])
>>> ak.squeeze(x,axis=None).shape
(10,)
>>> ak.squeeze(x,axis=2)
array([array([0 1 2 3 4 5 6 7 8 9])])
>>> ak.squeeze(x,axis=2).shape
(1, 10)
>>> ak.squeeze(x,axis=(0,2))
array([0 1 2 3 4 5 6 7 8 9])
>>> ak.squeeze(x,axis=(0,2)).shape
(10,)

arkouda.standard_normal(size: arkouda.numpy.dtypes.int_scalars, seed: None | arkouda.numpy.dtypes.int_scalars = None) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Draw real numbers from the standard normal distribution.

Parameters:

size (int_scalars) – The number of samples to draw (size of the returned array)
seed (int_scalars) – Value used to initialize the random number generator

Returns:

The array of random numbers

Return type:

pdarray, float64

Raises:

TypeError – Raised if size is not an int
ValueError – Raised if size < 0

See also

randint

Notes

For random samples from $N(\mu, \sigma^2)$, use:

(sigma * standard_normal(size)) + mu

Examples

>>> ak.standard_normal(3,1)
array([-0.68586185091150265 1.1723810583573377 0.567584107142031])

arkouda.std(pda: pdarray, ddof: arkouda.numpy.dtypes.int_scalars = 0) → numpy.float64[source]¶

Return the standard deviation of values in the array. The standard deviation is implemented as the square root of the variance.

Parameters:

pda (pdarray) – values for which to calculate the standard deviation
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating std

Returns:

The scalar standard deviation of the array

Return type:

np.float64

Examples

>>> a = ak.arange(10)
>>> ak.std(a)
2.8722813232690143
>>> a.std()
2.8722813232690143

Raises:

TypeError – Raised if pda is not a pdarray instance or ddof is not an integer
ValueError – Raised if ddof is an integer < 0
RuntimeError – Raised if there’s a server-side error thrown

See also

mean, var

Notes

The standard deviation is the square root of the average of the squared deviations from the mean, i.e., std = sqrt(mean((x - x.mean())**2)).

arkouda.str_scalars¶

arkouda.string_operators(cls)[source]¶

arkouda.tile(A: arkouda.numpy.pdarrayclass.pdarray, /, reps: int | Tuple[int, Ellipsis]) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Construct an array by repeating A the number of times given by reps.

If reps has length d, the result will have dimension of max(d, A.ndim).

If A.ndim < d, A is promoted to be d-dimensional by prepending new axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication, or shape (1, 1, 3) for 3-D replication. If this is not the desired behavior, promote A to d-dimensions manually before calling this function.

If A.ndim > d, reps is promoted to A.ndim by prepending 1’s to it. Thus for an A of shape (2, 3, 4, 5), a reps of (2, 2) is treated as (1, 1, 2, 2).

Parameters:

A (pdarray) – The input pdarray to be tiled
reps (int or Tuple of int) – The number of repetitions of A along each axis.

Returns:

A new pdarray with the tiled data.

Return type:

Examples

>>> a = ak.array([0, 1, 2])
>>> ak.tile(a, 2)
array([0 1 2 0 1 2])
>>> ak.tile(a, (2, 2))
array([array([0 1 2 0 1 2]) array([0 1 2 0 1 2])])
>>> ak.tile(a, (2, 1, 2))
array([array([array([0 1 2 0 1 2])]) array([array([0 1 2 0 1 2])])])

>>> b = ak.array([[1, 2], [3, 4]])
>>> ak.tile(b, 2)
array([array([1 2 1 2]) array([3 4 3 4])])
>>> ak.tile(b, (2, 1))
array([array([1 2]) array([3 4]) array([1 2]) array([3 4])])

>>> c = ak.array([1, 2, 3, 4])
>>> ak.tile(c, (4, 1))
array([array([1 2 3 4]) array([1 2 3 4]) array([1 2 3 4]) array([1 2 3 4])])

arkouda.timedelta_range(start=None, end=None, periods=None, freq=None, name=None, closed=None, **kwargs)[source]¶

Return a fixed frequency TimedeltaIndex, with day as the default frequency. Alias for ak.Timedelta(pd.timedelta_range(args)). Subject to size limit imposed by client.maxTransferBytes.

Parameters:

start (str or timedelta-like, default None) – Left bound for generating timedeltas.
end (str or timedelta-like, default None) – Right bound for generating timedeltas.
periods (int, default None) – Number of periods to generate.
freq (str or DateOffset, default 'D') – Frequency strings can have multiples, e.g. ‘5H’.
name (str, default None) – Name of the resulting TimedeltaIndex.
closed (str, default None) – Make the interval closed with respect to the given frequency to the ‘left’, ‘right’, or both sides (None).

Returns:

rng

Return type:

TimedeltaIndex

Notes

Of the four parameters start, end, periods, and freq, exactly three must be specified. If freq is omitted, the resulting TimedeltaIndex will have periods linearly spaced elements between start and end (closed on both sides).

To learn more about the frequency strings, please see this link.

arkouda.timedelta_range(start=None, end=None, periods=None, freq=None, name=None, closed=None, **kwargs)[source]¶

Return a fixed frequency TimedeltaIndex, with day as the default frequency. Alias for ak.Timedelta(pd.timedelta_range(args)). Subject to size limit imposed by client.maxTransferBytes.

Parameters:

start (str or timedelta-like, default None) – Left bound for generating timedeltas.
end (str or timedelta-like, default None) – Right bound for generating timedeltas.
periods (int, default None) – Number of periods to generate.
freq (str or DateOffset, default 'D') – Frequency strings can have multiples, e.g. ‘5H’.
name (str, default None) – Name of the resulting TimedeltaIndex.
closed (str, default None) – Make the interval closed with respect to the given frequency to the ‘left’, ‘right’, or both sides (None).

Returns:

rng

Return type:

TimedeltaIndex

Notes

To learn more about the frequency strings, please see this link.

arkouda.to_csv(columns: Mapping[str, arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings] | List[arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings], prefix_path: str, names: List[str] | None = None, col_delim: str = ',', overwrite: bool = False)[source]¶

Write Arkouda object(s) to CSV file(s). All CSV Files written by Arkouda include a header denoting data types of the columns.

Parameters:

columns (Mapping[str, pdarray] or List[pdarray]) – The objects to be written to CSV file. If a mapping is used and names is None the keys of the mapping will be used as the dataset names.
prefix_path (str) – The filename prefix to be used for saving files. Files will have _LOCALE#### appended when they are written to disk.
names (List[str] (Optional)) – names of dataset to be written. Order should correspond to the order of data provided in columns.
col_delim (str) – Defaults to “,”. Value to be used to separate columns within the file. Please be sure that the value used DOES NOT appear in your dataset.
overwrite (bool) – Defaults to False. If True, any existing files matching your provided prefix_path will be overwritten. If False, an error will be returned if existing files are found.

Return type:

None

Raises:

ValueError – Raised if any datasets are present in all csv files or if one or more of the specified files do not exist
RuntimeError – Raised if one or more of the specified files cannot be opened. If allow_errors is true this may be raised if no values are returned from the server.
TypeError – Raised if we receive an unknown arkouda_type returned from the server

See also

read_csv

Notes

CSV format is not currently supported by load/load_all operations
The column delimiter is expected to be the same for column names and data
Be sure that column delimiters are not found within your data.
All CSV files must delimit rows using newline (\n) at this time.
Unlike other file formats, CSV files store Strings as their UTF-8 format instead of storing bytes as uint(8).

arkouda.to_hdf(columns: Mapping[str, arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray] | List[arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray], prefix_path: str, names: List[str] | None = None, mode: str = 'truncate', file_type: str = 'distribute') → None[source]¶

Save multiple named pdarrays to HDF5 files.

Parameters:

columns (dict or list of pdarrays) – Collection of arrays to save
prefix_path (str) – Directory and filename prefix for output files
names (list of str) – Dataset names for the pdarrays
mode ({'truncate' | 'append'}) – By default, truncate (overwrite) the output files if they exist. If ‘append’, attempt to create new dataset in existing files.
file_type (str ("single" | "distribute")) – Default: distribute Single writes the dataset to a single file Distribute writes the dataset to a file per locale

Return type:

None

Raises:

ValueError – Raised if (1) the lengths of columns and values differ or (2) the mode is not ‘truncate’ or ‘append’
RuntimeError – Raised if a server-side error is thrown saving the pdarray

Notes

Examples

>>> a = ak.arange(25)
>>> b = ak.arange(25)

>>> # Save with mapping defining dataset names
>>> ak.to_hdf({'a': a, 'b': b}, 'path/name_prefix')

>>> # Save using names instead of mapping
>>> ak.to_hdf([a, b], 'path/name_prefix', names=['a', 'b'])

arkouda.to_parquet(columns: Mapping[str, arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray] | List[arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray], prefix_path: str, names: List[str] | None = None, mode: str = 'truncate', compression: str | None = None, convert_categoricals: bool = False) → None[source]¶

Save multiple named pdarrays to Parquet files.

Parameters:

columns (dict or list of pdarrays) – Collection of arrays to save
prefix_path (str) – Directory and filename prefix for output files
names (list of str) – Dataset names for the pdarrays
mode ({'truncate' | 'append'}) – By default, truncate (overwrite) the output files if they exist. If ‘append’, attempt to create new dataset in existing files. ‘append’ is deprecated, please use the multi-column write
compression (str (Optional)) –

Default None
Provide the compression type to use when writing the file. Supported values: snappy, gzip, brotli, zstd, lz4

convert_categoricals: bool
Defaults to False Parquet requires all columns to be the same size and Categoricals don’t satisfy that requirement. if set, write the equivalent Strings in place of any Categorical columns.

Return type:

None

Raises:

ValueError – Raised if (1) the lengths of columns and values differ or (2) the mode is not ‘truncate’ or ‘append’
RuntimeError – Raised if a server-side error is thrown saving the pdarray

See also

to_hdf, load, load_all, read

Notes

Creates one file per locale containing that locale’s chunk of each pdarray. If columns is a dictionary, the keys are used as the Parquet column names. Otherwise, if no names are supplied, 0-up integers are used. By default, any existing files at path_prefix will be deleted (regardless of whether they would be overwritten), unless the user specifies the ‘append’ mode, in which case arkouda will attempt to add <columns> as new datasets to existing files. If the wrong number of files is present or dataset names already exist, a RuntimeError is raised.

Examples

>>> a = ak.arange(25)
>>> b = ak.arange(25)

>>> # Save with mapping defining dataset names
>>> ak.to_parquet({'a': a, 'b': b}, 'path/name_prefix')

>>> # Save using names instead of mapping
>>> ak.to_parquet([a, b], 'path/name_prefix', names=['a', 'b'])

arkouda.to_zarr(store_path: str, arr: arkouda.numpy.pdarrayclass.pdarray, chunk_shape)[source]¶

Writes a pdarray to disk as a Zarr store. Supports multi-dimensional pdarrays of numeric types. To use this function, ensure you have installed the blosc dependency (make install-blosc) and have included ZarrMsg.chpl in the ServerModules.cfg file.

Parameters:

store_path (str) – The path at which Zarr store should be written
arr (pdarray) – The pdarray to be written to disk
chunk_shape (tuple) – The shape of the chunks to be used in the Zarr store

Raises:

ValueError – Raised if the number of dimensions in the chunk shape does not match the number of dimensions in the array or if the array is not a 32 or 64 bit numeric type

arkouda.typechecked(func=None, *, always=False, _localns: Dict[str, Any] | None = None)[source]¶

Perform runtime type checking on the arguments that are passed to the wrapped function.

The return value is also checked against the return annotation if any.

If the __debug__ global variable is set to False, no wrapping and therefore no type checking is done, unless always is True.

This can also be used as a class decorator. This will wrap all type annotated methods in the class with this decorator.

Parameters:

func – the function or class to enable type checking for
always – True to enable type checks even in optimized mode

arkouda.uniform(size: arkouda.numpy.dtypes.int_scalars, low: arkouda.numpy.dtypes.numeric_scalars = float(0.0), high: arkouda.numpy.dtypes.numeric_scalars = 1.0, seed: None | arkouda.numpy.dtypes.int_scalars = None) → arkouda.numpy.pdarrayclass.pdarray[source]¶

Generate a pdarray with uniformly distributed random float values in a specified range.

Parameters:

low (float_scalars) – The low value (inclusive) of the range, defaults to 0.0
high (float_scalars) – The high value (inclusive) of the range, defaults to 1.0
size (int_scalars) – The length of the returned array
seed (int_scalars, optional) – Value used to initialize the random number generator

Returns:

Values drawn uniformly from the specified range

Return type:

pdarray, float64

Raises:

TypeError – Raised if dtype.name not in DTypes, size is not an int, or if either low or high is not an int or float
ValueError – Raised if size < 0 or if high < low

Notes

The logic for uniform is delegated to the ak.randint method which is invoked with a dtype of float64

Examples

>>> ak.uniform(3,seed=1701)
array([0.011410423448327005 0.73618171558685619 0.12367222192448891])

>>> ak.uniform(size=3,low=0,high=5,seed=0)
array([0.30013431967121934 0.47383036230759112 1.0441791878997098])

arkouda.union1d(A: arkouda.groupbyclass.groupable, B: arkouda.groupbyclass.groupable) → arkouda.groupbyclass.groupable[source]¶

Find the union of two arrays/List of Arrays.

Return the unique, sorted array of values that are in either of the two input arrays.

Parameters:

A (list of pdarrays, pdarray, Strings, or Categorical)
B (list of pdarrays, pdarray, Strings, or Categorical)

Returns:

Unique, sorted union of the input arrays.

Return type:

pdarray/groupable

Raises:

TypeError – Raised if either A or B is not a groupable
RuntimeError – Raised if the dtype of either input is not supported

Examples

1D Example

>>> ak.union1d(ak.array([-1, 0, 1]), ak.array([-2, 0, 2]))
array([-2 -1 0 1 2])

Multi-Array Example

>>> a = ak.arange(1, 6)
>>> b = ak.array([1, 5, 3, 4, 2])
>>> c = ak.array([1, 4, 3, 2, 5])
>>> d = ak.array([1, 2, 3, 5, 4])
>>> multia = [a, a, a]
>>> multib = [b, c, d]
>>> ak.union1d(multia, multib)
[array([1 2 2 3 4 4 5 5]), array([1 2 5 3 2 4 4 5]), array([1 2 4 3 5 4 2 5])]

arkouda.unique(pda: groupable, return_groups: bool = False, assume_sorted: bool = False, return_indices: bool = False) → groupable | Tuple[groupable, pdarray, pdarray, int][source]¶

Find the unique elements of an array.

Returns the unique elements of an array, sorted if the values are integers. There is an optional output in addition to the unique elements: the number of times each unique value comes up in the input array.

Parameters:

pda ((list of) pdarray, Strings, or Categorical) – Input array.
return_groups (bool, optional) – If True, also return grouping information for the array.
assume_sorted (bool, optional) – If True, assume pda is sorted and skip sorting step
return_indices (bool, optional) – Only applicable if return_groups is True. If True, return unique key indices along with other groups

Returns:

unique ((list of) pdarray, Strings, or Categorical) – The unique values. If input dtype is int64, return values will be sorted.
permutation (pdarray, optional) – Permutation that groups equivalent values together (only when return_groups=True)
segments (pdarray, optional) – The offset of each group in the permuted array (only when return_groups=True)

Raises:

TypeError – Raised if pda is not a pdarray or Strings object
RuntimeError – Raised if the pdarray or Strings dtype is unsupported

Notes

For integer arrays, this function checks to see whether pda is sorted and, if so, whether it is already unique. This step can save considerable computation. Otherwise, this function will sort pda.

Examples

>>> A = ak.array([3, 2, 1, 1, 2, 3])
>>> ak.unique(A)
array([1, 2, 3])

arkouda.unique(pda: groupable, return_groups: bool = False, assume_sorted: bool = False, return_indices: bool = False) → groupable | Tuple[groupable, pdarray, pdarray, int][source]¶

Find the unique elements of an array.

Parameters:

pda ((list of) pdarray, Strings, or Categorical) – Input array.
return_groups (bool, optional) – If True, also return grouping information for the array.
assume_sorted (bool, optional) – If True, assume pda is sorted and skip sorting step
return_indices (bool, optional) – Only applicable if return_groups is True. If True, return unique key indices along with other groups

Returns:

unique ((list of) pdarray, Strings, or Categorical) – The unique values. If input dtype is int64, return values will be sorted.
permutation (pdarray, optional) – Permutation that groups equivalent values together (only when return_groups=True)
segments (pdarray, optional) – The offset of each group in the permuted array (only when return_groups=True)

Raises:

TypeError – Raised if pda is not a pdarray or Strings object
RuntimeError – Raised if the pdarray or Strings dtype is unsupported

Notes

For integer arrays, this function checks to see whether pda is sorted and, if so, whether it is already unique. This step can save considerable computation. Otherwise, this function will sort pda.

Examples

>>> A = ak.array([3, 2, 1, 1, 2, 3])
>>> ak.unique(A)
array([1, 2, 3])

arkouda.unregister(name: str) → str[source]¶

arkouda.unregister_all(names: list)[source]¶

Unregister all names provided

Parameters:: names (list) – List of names used to register objects to be unregistered
Return type:: None

arkouda.unregister_pdarray_by_name(user_defined_name: str) → None[source]¶

Unregister a named pdarray in the arkouda server which was previously registered using register() and/or attahced to using attach_pdarray()

Parameters:: user_defined_name (str) – user defined name which array was registered under
Return type:: None
Raises:: RuntimeError – Raised if the server could not find the internal name/symbol to remove

Notes

Registered names/pdarrays in the server are immune to deletion until they are unregistered.

Examples

>>> a = zeros(100)
>>> a.register("my_zeros")
>>> # potentially disconnect from server and reconnect to server
>>> b = ak.attach_pdarray("my_zeros")
>>> # ...other work...
>>> ak.unregister_pdarray_by_name(b)

arkouda.update_hdf(columns: Mapping[str, arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray] | List[arkouda.numpy.pdarrayclass.pdarray | arkouda.numpy.strings.Strings | arkouda.numpy.segarray.SegArray], prefix_path: str, names: List[str] | None = None, repack: bool = True)[source]¶

Overwrite the datasets with name appearing in names or keys in columns if columns is a dictionary

Parameters:

columns (dict or list of pdarrays) – Collection of arrays to save
prefix_path (str) – Directory and filename prefix for output files
names (list of str) – Dataset names for the pdarrays
repack (bool) – Default: True HDF5 does not release memory on delete. When True, the inaccessible data (that was overwritten) is removed. When False, the data remains, but is inaccessible. Setting to false will yield better performance, but will cause file sizes to expand.

Raises:

RuntimeError – Raised if a server-side error is thrown saving the datasets

Notes

If file does not contain File_Format attribute to indicate how it was saved, the file name is checked for _LOCALE#### to determine if it is distributed.
If the datasets provided do not exist, they will be added
Because HDF5 deletes do not release memory, this will create a copy of the file with the new data
This workflow is slightly different from to_hdf to prevent reading and creating a copy of the file for each dataset

arkouda.var(pda: pdarray, ddof: arkouda.numpy.dtypes.int_scalars = 0) → numpy.float64[source]¶

Return the variance of values in the array.

Parameters:

pda (pdarray) – Values for which to calculate the variance
ddof (int_scalars) – “Delta Degrees of Freedom” used in calculating var

Returns:

The scalar variance of the array

Return type:

np.float64

Examples

>>> a = ak.arange(10)
>>> ak.var(a)
8.25
>>> a.var()
8.25

Raises:

TypeError – Raised if pda is not a pdarray instance
ValueError – Raised if the ddof >= pdarray size
RuntimeError – Raised if there’s a server-side error thrown

See also

mean, std

Notes

The variance is the average of the squared deviations from the mean, i.e., var = mean((x - x.mean())**2).

arkouda.vstack(tup: Tuple[arkouda.numpy.pdarrayclass.pdarray] | List[arkouda.numpy.pdarrayclass.pdarray], *, dtype: type | str | None = None, casting: Literal['no', 'equiv', 'safe', 'same_kind', 'unsafe'] = 'same_kind') → arkouda.numpy.pdarrayclass.pdarray[source]¶

Stack a sequence of arrays vertically (row-wise).

This is equivalent to concatenation along the first axis after 1-D arrays of shape (N,) have been reshaped to (1,N).

Parameters:

tup (Tuple[pdarray]) – The arrays to be stacked
dtype (Optional[Union[type, str]], optional) – The data-type of the output array. If not provided, the output array will be determined using np.common_type on the input arrays Defaults to None
casting ({"no", "equiv", "safe", "same_kind", "unsafe"], optional) – Controls what kind of data casting may occur - currently unused

Returns:

pdarray – The stacked array

arkouda.write_log(log_msg: str, tag: str = 'ClientGeneratedLog', log_lvl: LogLevel = LogLevel.INFO)[source]¶

Allows the user to write custom logs.

Parameters:

log_msg (str) – The message to be added to the server log
tag (str) – The tag to use in the log. This takes the place of the server function name. Allows for easy identification of custom logs. Defaults to “ClientGeneratedLog”
log_lvl (LogLevel) – The type of log to be written Defaults to LogLevel.INFO

See also

LogLevel

arkouda.xlogy(x: arkouda.numpy.pdarrayclass.pdarray | numpy.float64, y: arkouda.numpy.pdarrayclass.pdarray)[source]¶

Computes x * log(y).

Parameters:

x (pdarray or np.float64) – x must have a datatype that is castable to float64
y (pdarray)

Return type:

Examples

>>> import arkouda as ak
>>> ak.connect()
>>> from arkouda.scipy.special import xlogy
>>> xlogy( ak.array([1, 2, 3, 4]),  ak.array([5,6,7,8]))
array([1.6094379124341003 3.5835189384561099 5.8377304471659395 8.317766166719343])
>>> xlogy( 5.0, ak.array([1, 2, 3, 4]))
array([0.00000000000000000 3.4657359027997265 5.4930614433405491 6.9314718055994531])

Create a pdarray filled with zeros.

Parameters:

size (int_scalars or tuple of int_scalars) – Size or shape of the array
dtype (all_scalars) – Type of resulting array, default ak.float64
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays Included for consistency, as zeros are represented as all zeros, regardless of the value of max_bits

Returns:

Zeros of the requested size or shape and dtype

Return type:

Raises:

TypeError – Raised if the supplied dtype is not supported
RuntimeError – Raised if the size parameter is neither an int nor a str that is parseable to an int.
ValueError – Raised if the rank of the given shape is not in get_array_ranks() or is empty Raised if max_bits is not NONE and ndim does not equal 1

See also

ones, zeros_like

Examples

>>> ak.zeros(5, dtype=ak.int64)
array([0 0 0 0 0])

>>> ak.zeros(5, dtype=ak.float64)
array([0.00000000000000000 0.00000000000000000 0.00000000000000000
       0.00000000000000000 0.00000000000000000])

>>> ak.zeros(5, dtype=ak.bool_)
array([False False False False False])

Create a pdarray filled with zeros.

Parameters:

size (int_scalars or tuple of int_scalars) – Size or shape of the array
dtype (all_scalars) – Type of resulting array, default ak.float64
max_bits (int) – Specifies the maximum number of bits; only used for bigint pdarrays Included for consistency, as zeros are represented as all zeros, regardless of the value of max_bits

Returns:

Zeros of the requested size or shape and dtype

Return type: